Systems and methods for detecting and managing physiological patterns

ABSTRACT

Systems and methods for managing sleep quality of a patient, comprising: collecting physiological signal data of the patient using a data acquisition unit electrically coupled to at least one sensor affixed to the patient that generates the physiologic signal data; using one or more hardware processors executing instructions stored in a storage device: filtering the physiological signal data into a plurality of frequency bands corresponding to a plurality of power spectra waveforms; and characterizing an etiology of sleep quality of the patient based on a comparison of at least a first power spectra waveform of the plurality of power spectra waveforms against at least a second power spectra waveform of the plurality of power spectra waveforms, wherein the sleep quality of the patient is managed based on the characterized etiology of sleep.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent App. No.62/508,221, filed on May 18, 2017, and titled “DETECTING AND MANAGINGNORMAL AND ABNORMAL NEUROPHYSIOLOGICAL PATTERNS,” and U.S. ProvisionalPatent App. No. 62/620,236, filed on Jan. 22, 2018, and titled“DETECTING AND MANAGING NORMAL AND ABNORMAL NEUROPHYSIOLOGICALPATTERNS,” both of which are incorporated herein by reference in theirentireties.

BACKGROUND Field of the Invention

The embodiments described herein are generally directed to the field ofmonitoring sleep architecture and more specifically to systems andmethods for monitoring and detecting abnormal physiological signalpatterns.

Description of the Related Art

Sleep is important to our physical and mental health. The quality andquantity of sleep we obtain impacts our risk for development of chronicdiseases, neurodegeneration and mood disorders, and influences the speedof recovery from a hospitalized illness. The electroencephalography(EEG) is commonly used to characterize sleep traditional criteria forstaging of epochs of selected time scale (e.g., 30 seconds) into awake;stage N1, NREM (sometimes referred to either N2, N3); or rapid eyemovement (REM) sleep. Sleep in intensive care units (ICU), emergencyrooms, or other hospital environments may be difficult due to theenvironmental noise and other factors, resulting in sleep-wake cycle(circadian) disruptions. For example, when the circadian rhythm isdisrupted, patients may sleep intermittently during the day and night,rather than having their sleep consolidated during nocturnal hours. Thisdisruption may impair recovery times or lead to additional aliments.Continuous monitoring may then needed to measure the total amount andtiming of sleep obtained in a 24-hr cycle. Detecting objective signs ofsleep in hospital environments for applying visual scoring techniques toEEG traces may be complicated in part due to disruption of the circadianrhythms. Further complicating the accurate measurement of sleep in theICU are atypical EEG patterns that inhibited the application of thestandard sleep staging rules to EEG signals acquired from ICU patients.

A conventional approach for improving sleep quality for ICU patients isto induce more normal sleep-wake cycles through administration ofsedatives according to circadian time (e.g., higher dosage at night).Optimizing ICU sleep around the circadian rhythm can decrease theduration of mechanical ventilation, intubation time, and the length ofICU stay. It can also decrease the amount of sedative drugs used duringthe day, and reduce the incidence of delirium.

Another cause of sleep disruption in the ICU may be mechanicalventilation, in part as, a result of ineffective patient-ventilatorinteractions. Patient ventilatory asynchrony impacts as many as 25% ofmechanically ventilated patients in the ICU, and contributes to sleepfragmentation, higher sedation levels, delirium, lung injury, prolongedmechanical ventilation and mortality. Sleep architecture may be highlyabnormal in mechanically ventilated patients, with decreased REM timeand high sleep fragmentation and that three optional types ofventilatory modes may not influence the arousals awakenings or have anineffective effect. Conversely, patient ventilator discordance may causesleep disruption, and that proportional assist ventilation may be moreefficacious than pressure support ventilation. Neutrally adjustedventilatory assist (NAVA) may contribute to improved REM sleep, lessfragmented sleep, and more effective effort as compared to pressuresupport ventilation in non-sedated patients. However, NAVA involvesinsertion of a nasogastric tube mounted with electrode rings to measurethe electrical activity of the diaphragm so to obtain a signal that canused to assess dyssynchrony. As an alternative to insertion of acatheter, surface EMG processing may assist in assessing inspiratorydrive during mechanical ventilation.

EEG monitoring has primarily focused on the identification ofepilepticus waveform/seizure activity, burst suppression, and/orcoma-like patterns in the brains electrical activity. Traditionally,long term EEG monitoring (e.g., 24-hour), as opposed to short termmonitoring (2-4 hours), has been necessary to identify patients withnon-convulsive seizures and periodic epileptiform discharges. Theassessment of sleep in the ICU has only been conducted on a researchbasis due in part to the difficulty of visually staging sleep. Thevisual characteristics of abnormal large amplitude slow waves whichappear during both sleep and awake in the ICU and can be incorrectlyassigned stage N3 due to the signal shape. Both polymorphic deltaactivity and frontal intermittent rhythmic delta activity were detectedin ICU EEG measurements.

It is believed that polymorphic delta activity reflecting low-levelrandom inputs to cortical networks, while frontal intermittent rhythmicdelta activity (FIRDA) reflected limited-cycle oscillations due toincreased excitation. There may be strong relationship betweenpolymorphic delta activity and abnormal cerebral white matter associatedwith seizures, ischemia/stroke and other causes. Polymorphic deltaactivity may reflect disturbed neural activity within the fullfunctionality of the brain network. Additionally, cardiac output instroke patients may be contributed to the generation of FIRDA.Asymmetric FIRDA may also be related to brain lesions and FIRDA may beassociated with high risk acute non-convulsive seizure activity. FIRDAhas been detected principally in awake patients and occurred in patientswith chronic systemic illness.

Burst suppression is another common EEG pattern in ICU patients. Burstsuppression in the EEG may be an independent predictor of increased riskof patient death at 6 months. Time in burst suppression during coma mayalso be an independent predictor of prevalence and time to resolution ofpost-coma delirium.

Sepsis-associated encephalopathy (SAE) may result from direct cellulardamage to the brain, mitochondrial and endothelial dysfunction,neurotransmission disturbances and derangements of calcium homeostasisin the brain tissue. SAE mechanisms may be highly complex, resultingfrom both inflammatory and non-inflammatory processes that affect allbrain cells and induce blood-brain barrier breakdown, dysfunction ofintracellular metabolism, brain cell death, and brain injuries. Thediagnosis of SAE relies on application of exclusion criteria that canlead to specific neurologic tests, including an EEG.

In some cases SAE may precede the cardinal finding of sepsis, acondition which accounts for up to 50% of the deaths in the ICU. EEGpatterns of low-voltage mixed-frequency waves with intermittent amountsof theta and delta waveform activity may be apparent when a patient'seyes are both open and closed, up to 8 hours prior to patientsdemonstrating clinical signs of sepsis. Triphasic waves and suppressionare two EEG patterns that can be found in patients with the most severeform of sepsis. Additional patterns of SAE have been described asdiffuse delta waves (<4 Hz) and generalized burst suppression pattern(alternating diffuse reductions in voltage with burst of higher voltagewaves). Sepsis-related brain dysfunction may also includesepsis-associated delirium (SAD), suggesting SAE is an early feature ofthe infection, and abnormal EEG may assist the clinician in defining theseverity of SAD. Furthermore, decreased EEG alpha activity has beenidentified as a biomarker of septic encephalopathy in rats, and may notinclude the comparison to relative power or the beneficial inclusion ofrelative or absolute delta, theta, beta, or gamma power.

Patients with mental confusion, or altered wakefulness, may benefit froman evaluation of EEG for detection of non-convulsive seizure activity.At least four conditions have been identified that may benefit fromemergency room EEG: evaluation of consciousness or prolonged impairmentof consciousness, and/or suspected subclinical or subtle seizureactivity, or seizure activity during administration of muscle relaxantsfor endotracheal intubation. Other conditions may also benefit.

Monitoring burst suppressions may be automated using clustering patternrecognition techniques, for example, for patients in an induced coma.The ratio between alpha and delta activity may be applied todifferentiate polymorphic delta activity in the acute and chronic strokephases of rats. The alpha/sigma ratio may be associated with mortality,sedatives and sepsis. Sepsis may also be associated with an abnormaldelta/theta ratio.

A pattern of persistent rhythmic waves or persistent high-amplitude slowwaves (<2 Hz) may be obtained with two bipolar left and right leads.

A number of EEG recording systems have been developed. For example, awireless device has been developed that acquires using dry electrodefrom a limited montage (Fz, C3, Cz, C4 and Pz). As another example, anEEG system includes an elastic head strap, electrodes and a wirelesstransmitter, able to acquire EEG from the central and temporal regions.In another example, a wireless EEG acquisition device is provided thatis intended for point-of-care applications (e.g., emergency room). Animage detection system detects delirium. Many devices have appliedbilateral brain monitoring for sedation or anesthesia monitoring. Awireless recorder/monitor has been affixed to the head or forehead of apatient that provides the capability to monitor sleep architecture andcontinuity.

However, EEG is not routinely monitored in hospitalized patients orpatients admitted to the ICU. This is because a trained EEG technicianis needed to apply the full montage, continuous EEG acquisition system.Additionally, these conventional EEG acquisition systems are large andexpensive, and thus further limit routine monitoring on all patients asa precaution. Another limitation of conventional EEG is that an EEGtechnician and/or neurologist is needed to monitor the signals in realtime to detect abnormal patterns.

SUMMARY

Systems and method for management of sleep quality of a patient areprovided herein.

In an embodiment, a method for managing sleep quality of a patient in,for example, a hospital environment such as an emergency room orintensive care unit is provided. The method comprises collectingphysiological signal data of the patient using a data acquisition unitelectrically coupled to at least one sensor affixed to the patient thatgenerates the physiologic signal data. The method also comprises, usingone or more hardware processors executing instructions stored in astorage device, filtering the physiological signal data into a pluralityof frequency bands corresponding to a plurality of power spectrawaveforms; and characterizing an etiology of sleep quality of thepatient based on a comparison of at least a first power spectra waveformof the plurality of power spectra waveforms against at least a secondpower spectra waveform of the plurality of power spectra waveforms,wherein the sleep quality of the patient is managed based on thecharacterized etiology of sleep.

In another embodiment, a system for managing sleep quality of a patientis provided. The system comprises a data acquisition unit electricallycoupled to at least one sensor affixed to the patient. The dataacquisition unit collects physiological signal data of the patientgenerated by the at least on sensor. The system also comprises at leastone hardware processor, and a storage device coupled to the at least onehardware processor and the data acquisition unit. The storage devicestores instructions that, when executed by the at least one hardware,are operable to filter the physiological signal data into a plurality offrequency bands corresponding to a plurality of power spectra waveforms,and characterize an etiology of sleep quality of the patient based on acomparison of at least a first power spectra waveform of the pluralityof power spectra waveforms against at least a second power spectrawaveform of the plurality of power spectra waveforms, wherein the sleepquality of the patient is managed based on the characterized etiology ofsleep.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present invention, both as to its structure andoperation, may be gleaned in part by study of the accompanying drawings,in which like reference numerals refer to like parts, and in which:

FIG. 1 illustrates a patient with a data acquisition system including adata acquisition unit according to an embodiment;

FIGS. 2A-2C illustrates example enclosures of data acquisition units andinterface with the sensor strap according to various embodiments;

FIG. 3 is a block diagram identifying functional components and circuitsof a data acquisition unit according to an embodiment;

FIG. 4 illustrates an example processing device on which one or more ofthe processes described herein may be executed, according to anembodiment

FIG. 5 schematically illustrates an integrated system for monitoringphysiological signal patterns of one or more patients, in accordancewith various embodiments;

FIG. 6 illustrates a flowchart for an example process of detectingabnormal signal patterns, in accordance with various embodiments;

FIG. 7 illustrates a flowchart for an example process for diagnosis andtherapeutic treatment of patients in accordance with variousembodiments;

FIG. 8 is a flow chart of a method for monitoring physiological signalsto speed recovery and improve outcomes, in accordance with embodimentsherein;

FIGS. 9A-11C include data illustrating an example characterizations ofacquired physiological signal patterns according to embodiments herein;

FIG. 12 is a flow chart of a method for modifying standard sleep stagingrules, in accordance with embodiments herein;

FIG. 13 is a flow chart of a method for monitoring physiological signalpatterns to identify signal patterns resulting in poor quality sleep, inaccordance with embodiments herein;

FIGS. 14A-14B include data illustrating an example of physiologicalsignal patterns indicative of sleep disordered breathing, in accordancewith embodiments herein;

FIG. 15 is a flow chart of a method for detection of abnormal slow waveactivity, in accordance with embodiments herein;

FIG. 16 includes data illustrating an example of physiological signalpatterns indicative of abnormal slow wave activity, in accordance withembodiments herein;

FIG. 17 includes data illustrating an example of physiological signalpatterns indicative of frontal intermittent rhythmic delta activity, inaccordance with embodiments herein;

FIG. 18 includes data illustrating another example of physiologicalsignal patterns indicative of abnormal slow wave activity, in accordancewith embodiments herein;

FIGS. 19A and 19B include data of an example of physiological signalpatterns an elevated by steady sound experienced by the patient, inaccordance with embodiments herein;

FIGS. 20A-20D include data of an example of physiological signalpatterns EMG burst patterns indicative of ventilator distress or centralsleep apnea associated arousals, in accordance with embodiments herein;

FIGS. 21A-23B include data of examples of physiological signal patternsof abnormal burst suppression, in accordance with embodiments herein;

FIGS. 24A-24C include data of example physiological signal patternsindicative of non-convulsive seizure activity, in accordance withembodiments herein;

FIGS. 25A-25E illustrates data of an example of physiological data withtransitions across different abnormal physiological signal patterns, inaccordance with embodiments herein;

FIGS. 26 and 27 are flow charts of example methods for monitoring apatient's circadian rhythm to improve the quality of sleep, inaccordance with embodiments herein;

FIG. 28 is a flow chart of an example method for reducing the likelihoodof developing symptoms of post-traumatic stress syndrome, in accordancewith embodiments herein; and

FIGS. 29-31 illustrate embodiments of a graphical user interface fordisplaying physiological signal patterns, in accordance with embodimentsherein.

DETAILED DESCRIPTION

After reading this description, it will become apparent to one skilledin the art how to implement the invention in various alternativeembodiments and alternative applications. However, although variousembodiments of the present invention will be described herein, it isunderstood that these embodiments are presented by way of example andillustration only, and not limitation. As such, this detaileddescription of various embodiments should not be construed to limit thescope or breadth of the present invention as set forth in the appendedclaims.

The described systems and methods are based on the acquisition andanalysis of neurophysiological signals (also referred to herein as“physiological signals”). In some embodiments, the systems and methodsdescribed herein may provide for real-time monitoring of sleeparchitecture. In various embodiments, the systems and methods mayprovide for human and/or automated recognition of distinctivephysiological signal characteristics that may be used to triggerinterventions. Such interventions may be executed via human interactionand/or automated via computer or systems. Several embodiments utilize adata acquisition unit (DAU) that acquires and/or transmits physiologicalsignals from which of a quality of sleep of a patient may be monitoredand/or identified. These signals may be presented in a graphic userinterface in order to characterize the signals and quantify the patientssleep. Alternatively, the signals may be characterized throughprocessing techniques to identify and detect distinctive signal patternsindicative of abnormal conditions and/or sleep architecture. Suchcharacterization may be beneficial in hospitalized patients to monitorand stage the patients sleep patterns to improve recovery time and care.

The described systems combine automated detection of signal patternswith presentation techniques that may allow caregivers with limitedneurophysiological training or expertise to detect and differentiatenormal and abnormal physiological signal patterns. In an embodiment aDAU can be adapted for use as a periodic or continuous monitoring ofphysiologic signals. Various embodiments of the methods described hereinextract elements of normal and abnormal physiological signal patternsfor use in directing patient care. In one embodiment the patterndetection includes quantification of sleep architecture and sleepcontinuity for accurate detection of etiological sleep/wake (e.g.,etiology of sleep quality) in patients hospitalized, medicated, and/orcritically ill. The systems and methods also can include monitoringquantity of sleep, patterns of disruptions that will result in poorsleep quality, impact of interventions and/or medications, andeffectively managing abnormal neurological activity. Descriptions ofadditional means for detection of patterns of normal brain wave activityand abnormal brain wave activity associated with poor outcomes, sepsis,or mortality is provided. Various embodiments utilize a unique graphicaluser interface that improves presentation of both the physiologicalsignals and extracted features, while also displaying power spectralcharacteristics derived from the neurophysiological signals (e.g., EEG,EOG, ECG, etc.). Another aspect is that different signal featurecharacteristics can be viewed on different time scales selected tooptimize visual detection of the targeted signal patterns. These featurecharacteristics can be monitored either offline or in real time, andthat current or previously acquired data can be readily accessed andreviewed.

Various embodiments herein provide for the analysis and/or presentationof the physiological signals, sleep/wake, and power spectra as ascreening tool by users (e.g., non-experts and/or experts) that providefor improved detection of abnormal signal patterns. Such abnormal signalpatterns may include, but are not limited to, burst suppression andnon-convulsive epileptiform activity, which may necessitate patientsbeing placed on conventional (10-20 montage), continuous EEG monitoring.Further, the approaches described herein for remote or offsite viewingthat can also be applied to analyze and transmit the data signalsobtained from the continuous EEG monitoring system. The capability ofexperts to review the studies of different patients from differenthospitals, and from both screening and continuous monitoring systemswill improve the health and wellbeing of a greater number of patients,and improve the productivity of the experts.

As used herein, a “patient” may be a person from which physiologicalsignal data is collected therefrom, for example, using the systems anddevices described herein. Furthermore, as used herein a “user” may beany person or device that reviews, analysis, processes, evaluates, orotherwise interactions with collected data representative ofpsychological signals. For example, in some embodiments, a user may be ahealth-care provider, medical personnel, hospital employee, or the like.In another embodiment, alternatively or in combination, a user may referto a nurse, doctor, and/or specialist (e.g., an expert) of any givenmedical field. In yet another embodiment, a user may refer to a computerdevice and/or mobile device configured to process the collected data andperform some action in response thereto.

FIG. 1 illustrates a patient with the data acquisition system includinga data acquisition unit (DAU) 110 and a sensor strap 120. The dataacquisition system can be used to collect and store physiologicalsignals from a user while the user is sleeping in order to assess sleepquality, for example, an etiology of sleep. According to an embodiment,the data acquisition system can connect to an external computer systemthat is configured to process the data collected by the data acquisitionsystem (see FIG. 5 described below). In some embodiments, the dataacquisition system can be configured to perform at least some analysison the data collected before the data is downloaded to the externalcomputer system. The data acquisition system may be substantiallysimilar to the systems and devices described in detail in, for example,U.S. Pat. Nos. 8,355,769 and 8,639,313, both of which are incorporatedherein by reference. Embodiments described therein provide systems andmethod to acquire and/or transmit physiological signals used to assesssleep architecture and sleep continuity. For example, a DAU is describedthat can be used to acquire a number of physiological signals from theforehead including electroencephalographic (EEG), electroocular (EOG)and electromyographic (EMG) signals, head movement and position obtainedwith a 3D accelerometer, pulse rate, and snoring sounds measured with anacoustic microphone. Such implementations included concomitantmonitoring of respiratory patterns for the assessment of sleep andbreathing abnormality.

Referring again to FIG. 1, an embodiment of the data acquisition systemis illustrated comprising a headband 130, a sensor strap 120, anoptional nasal cannula 160, and a headband 130. In some embodiments, atop strap and/or nasal mask (not shown) may be optional. Furthermore,FIG. 1 illustrates the DAU 110 mounted directly over the sensor strap120 when affixed to the forehead, however in some embodiments the DAU110 may be positioned further up on the head. Either implementation isable to acquire signal data from the frontoplanar sites of the patient'sforehead. While example embodiments are illustrated herein, one skilledin the art will appreciate that the components of the data acquisitionsystem may be arranged in any combination of arrangements shown in FIG.1.

DAU 110 can be worn above the forehead of the patient and/or attached tothe sensor strap 120 during sleep to collect physiological signal data.In the embodiment illustrated in FIG. 1, the DAU 110 is integrated orcoupled with a sensor strap 120 and a nasal pneumotachometer (nowshown). An embodiment of the sensor strap 120 is described below inconnection with FIG. 2A. A headband 130 encircles the rear of thepatient's head to hold the data acquisition system in place. A top strapmay also extend over the back of the patient's head where it joins theheadband 130 for additional stability. Sensor strap 120 can be coupledto headband 130 to hold the sensor strap 120 in place over the user'sforehead.

According to an embodiment, the headband 130 and/or the top strap can beadjusted in size to accommodate users having different sized heads. Insome embodiments, can be removed and replaced with different sizedheadbands and top straps to accommodate different users. Furthermore,the headband and top straps can be designed to be one-time-usecomponents for sanitary purposes that can be removed while allowing thedata acquisition unit and/or other components of the apparatus to beused by another user.

In an embodiment, the DAU 110 includes physiological acquisition andstorage circuitry configured to assess sleep quality or record data foruse in assessing sleep quality. As described below, the assessment ofsleep quality includes performing concurrent measurements of a pluralityof categories of signal data, including but not limited to: (1) signaldata related to sleep states, and (2) signal data related to the type ofsleep disruption. DAU 110 is configured to perform the concurrentmeasurements of the sleep data, record these measurements, and in someembodiments, analyze and process the recorded data. In variousembodiments, the sensor strap 120 may be configured to acquire signaldata from less than a full montage (e.g., 10-20 montage) of theconventional EEG monitoring systems. For example, the sensor strap 120may acquire signals from the full 10-20 montage system sites of AF7, AF8and Fpz. The DAU 110 and sensor strap 120 can be used to implement themethods or as part of the systems described in, for example, FIGS. 4-31.According to some embodiments, DAU 110 can be positioned near the top ofthe head of the user or positioned over the forehead of the user (asillustrated in FIG. 1). The position of the DAU 110 can be based in parton the type of assessment to be performed and the types of sensor dataused to make that type of assessment.

According to an embodiment, sensor strap 120 may be removable, and insome embodiments, sensor strap 120 can also be disposable. For example,the sensor strap 120 can be configured to be electronically coupled tothe DAU 110 using a socket connection or other type of connection thatallows the sensor strap 120 to be removed and replaced. This can allowthe sensor strap to be replaced for sanitary purposes (as well as thetop strap and/or the headband, as described above) to allow the DAU 110to be used again with another user. In an embodiment, the sensor strap120 can be a one-time-use strip that is provided in a sealed sterilepackage. In some embodiments, elements of sensor strap 120 can bedisposable, while some components are reusable. For example, the sensorstrap 120 may include disposable EEG sensors and a reusable thepulse/oximetry sensor.

According to an embodiment, sensor strap 120 can also include anadhesive backing that helps to facilitate and maintain placement of thesensor strap 120 on the user's forehead region by removeably adhering tothe user's skin. In one embodiment, the sensor strap 120 can compriseadhesive backed foam. The adhesive backing can also help to maintainsensor contact with the user's skin for those sensors that require skincontact. According to some embodiments, conductive sensors included inthe sensor strap 120 can have a conductive gel placed over thesessensors. FIG. 2A, described below, illustrates some example of the typesof sensors than be included in the sensor strap 120. The configurationof the sensor strap 120 facilitates use of the data acquisition systemby users by making proper placement and attachment of sensors mucheasier than conventional systems. For example, some conventional EEGmonitoring systems require that numerous electrodes be affixed to apatient's head. Proper placement of the electrodes is important. As aresult, EEG data is often gathered in a clinical setting where theelectrodes can be affixed to the patient by a clinician. When performingsleep studies, this can have a negative impact on the results of thestudy, because the user is removed from his or her normal sleepingenvironment and placed into an unfamiliar clinical setting. The sensorstrap 120 used with the data acquisition systems disclosed hereinfacilitates home use of the device by making proper placement of thesensors easy for patients, thereby allowing users to gather data at homewhere they are likely to be more comfortable and more likely toexperience sleep episodes that are more typically of their regular sleepepisodes. Additionally, the sensor strap 120 used with the DAU disclosedherein facilitates hospital, emergency, ICU, out-patient, etc. use ofthe device by making proper placement of the sensor easy by caregiversthat lack specific expertise in performing sleep studies, therebyallowing caregivers to gather data in emergency situations and/orout-side of specifically designated clinical environments where thepatient may be more comfortable. Furthermore, this system permits thecaregivers to easily and inexpensively monitor the sleep episodes of thepatient in any given environment to improve recovery time.

FIGS. 2A-2C illustrate views of an enclosure of the DAU 110 and theinterface with sensor strap 120 according to various embodiments. TheDAU enclosure 200 includes a removable back cover 210 with a securingpush tab 220 that holds the sensor strap 120 in place during use.Removal of the back cover exposes the micro-USB connector 240 and heatdissipating vent holes 250 which allow for data transfer and batteryrecharging. According to an embodiment, the connectors that allow thedevice to be connected to an external power source, such as alternatingcurrent power from the mains power, are not accessible when the systembeing worn by a user. In an embodiment, electrical pathways between thesensors and the electronics can be interfaced with one touch-proofconnector for the ECG leads 260 and a connector 270 in the center of theenclosure 200 for the sensor strap.

In some embodiments the DAU 110 may comprise a nasal pressure transducerdisposed within the DAU enclosure 200 (e.g., FIGS. 2A and 2B).Alternatively, in some embodiments, a nasal pressure transducer 280 maybe affixed to the enclosure as illustrated in FIG. 2C and attached tothe optional nasal cannula 160 of FIG. 1. One non-limiting advantage ofthe optionally affixed nasal pressure transducer 280 is that it mayreduce the size and weight of the DAU 110 as well as reduce manufacturecomplexity and costs.

FIG. 3 is a block diagram identifying functional components and circuitsof a data acquisition apparatus for quantifying sleep quality accordingto an embodiment. In the illustrated embodiment, DAU 110 comprises ananalog-to-digital converter 312, an acoustic microphone 314, amicro-controller 315, an audio output 316 (e.g., speaker), anaccelerometer 317, a battery power component 318 (e.g., comprising apower supply 319 and/or battery 320), a sensor driving unit 323 (e.g.,comprising an optical signal amplifier that includes digitallyprogrammable potentiometers 321 and/or means to convert and amplifyoutputs from a photodiode 322), a data transfer module 326 (e.g.,comprising a data storage device or memory 324 and/or data transferinterface 325), and/or a wireless transmitter, receiver, or transceiver377. In addition, DAU 110 may be communicatively connected to a nasalpressure transducer 313, one or more EEG sensors 380, one or more ECGsensors 381, one or more other sensors 395 (e.g., via sensor drivingunit 323), and/or an external computer system 390 (e.g., via datatransfer module 326). It should be understood that, in alternativeembodiments, DAU 110 may have fewer, more, or different components(e.g., different types or combinations of sensors, embedded sensors, noacoustic microphone 314, no audio output 316, no accelerometer 317, nosensor driving unit 323, etc.), as well as a different arrangement ofcomponents (e.g., an external power supply), than those illustrated inFIG. 3.

The analog-to-digital converter 312 may provide for amplifying anddigitizing two channels of EEG/EOG data 310 for measuring sleeparchitecture and cortical arousals, and one channel of ECG data 311 toassess heart rate and autonomic/cortical arousals. According to otherembodiments, any combination of EEG channels could be employed. However,a single channel of EEG can reduce the accuracy of the sleep stagemeasurement and more than two channels can increase the size of the DAUwithout significantly increasing detection accuracy. The use of twochannels can significantly increase the system's ability todifferentiate REM from NREM sleep on the basis of rapid conjugate eyemovements that are characteristic of REM sleep and appear as largevoltage deflections that are out of phase in the two EEG channels.According to an embodiment, the EEG/EOG data 310 and EEG data can becaptured using electrodes integrated into sensor strap 120. FIG. 2A,which is described in detail above, provides an example embodiment ofone possible configuration of the sensor strap 120 that includesEEG/EOG/EMG electrodes for gathering the EEG/EOG data 310 and the ECGdata 311.

DAU 110 is configured to receive a signal from a nasal pressuretransducer 313 to acquire airflow data. The airflow data can be used inidentifying sleep disruptions, such as apnea. In an embodiment thedynamic range of the pressure transducer is set to optimize airflowresolution of (i.e., +/−2 cm/H20).

Acoustic microphone 314 can also be used to detect snoring and/or otheraudible symptoms that can be causing sleep disruption. DAU 110 includesan amplification circuit that receives and amplifies sound signals fromacoustic microphone 314. In some embodiments, the acoustic microphone314 can be integrated into the DAU 110, while in other embodiments, theacoustic microphone 314 can be included in the sensor strap 120 oraffixed to the headband 130. In an embodiment, a high fidelity sound issampled between 2 to 4 kilohertz to profile snoring pattern and torecognize the region of airway obstruction as well as assess nocturnalcoughing and wheezing. Alternatively, in some embodiments, snoringsounds can be quantified by rectification, integration, and sampling ata reduced frequency (e.g., 10 Hz) or with sensors limited to qualitativemeasures (e.g., vibration).

The DAU 110 includes an accelerometer 317 that can measure a full rangeof head positions, including both sleep and wake conditions, as well asbehavioral arousals defined by subtle head movements.

In the embodiment illustrated in FIG. 3, the DAU 110 includes a batterypower component 318 that includes a rechargeable lithium polymer battery320 and a power supply 319 and recharging circuitry for receiving powerfrom an external source for recharging battery 320 and/or powering theDAU 110. The battery power component 318 allows the DAU 110 to operatewithout requiring the DAU 110 to be tethered to an external power cordor power supply, which could be inconvenient and uncomfortable for auser of the device. According to some embodiments, an external powersupply can be used to power the device. According to other embodiments,battery 320 can be another type of battery 320 and in some embodimentsbattery 320 can be removable and replaceable.

A sensor driving unit 323 is included to provide a driving current todrive red and infrared light emitting diodes used in conjunction withsensors 395 to gather physiological data. The DAU 110 also includes anoptical signal amplifier that includes digitally programmablepotentiometers 321 and a means to convert and amplify outputs from aphotodiode 322. According to an embodiment, the sensors 395 can beincluded in the sensor strap 120.

The DAU 110 can include a storage device, e.g., a memory 324 for datastorage. In an embodiment, the memory 324 can comprise a removableMultimedia Memory or Secure Digital card or other types of removablepersistent memory. In another embodiment, the memory 324 can comprise afixed flash chip. According to an embodiment, a data transfer interface325 is provided. According to an embodiment, the data transfer interfacecomprises a USB data transfer chip. In another embodiment, USB transfercapabilities can be incorporated into micro-controller 315.

According to an embodiment, firmware is stored in a memory 324associated with micro-controller 315. According to an embodiment, thememory 324 is a flash memory. According to some embodiments, thefirmware can be updated via data transfer interface 325. Furthermore,according to some embodiments, the memory 324 and can be part of apersistent memory.

In an embodiment, the firmware is configured to routinely sample andsave signal data received by the DAU 110. According to an embodiment,filtering routines can be used to detect poor quality signal data and tonotify the user via an audible signal generated using audio output 316or via a piezo-electric buzzer. For example, if the user has misalignedthe position of the sensor strap 120 on the forehead, the signalsreceived from the sensor strap 120 may of poor quality. The DAU 110 cangenerate an audible alarm or vibrate if the sensor strap needs to berealigned.

In one embodiment, DAU 110 can include a wireless transmitter/receiver377 for receiving data from peripheral sensors (i.e., wireless ECGsensors, finger pulse oximeter, respiratory effort bands, sensorsmeasuring leg movements, etc.) and/or transmit signals to an externalcomputer system 390 for real time monitoring of the data being acquiredby the DAU 110. Data acquired from these sensors can be used todetermine the user's sleep architecture and/or to identify sleepdisruptions that can negatively impact sleep quality. In someembodiments, the wireless transmitter/receiver 377 can be integratedinto data transfer module 326 of DAU 110.

According to an embodiment, micro-controller 315 can be based on an ARM32-bit reduced instruction set computer (RISC) instruction set orequivalent architecture. Firmware can be configured to minimize thepower requirements of the ARM chip when the DAU is being used inrecording mode. The computational capacity of the ARM chip can providethe option for firmware to transform the signals during acquisition orprior to data download. For example, fast-Fourier transforms can beapplied to a 512 samples/second EEG signal can quantify the highfrequency power spectral densities of the EEG or EMG without requiringthe large data files to be transferred off line to make thiscomputation. Once high resolution power spectra are computed the EEG canbe saved at 64 samples/second for purposes of visual inspection. Giventhe preference to obtain high fidelity sound signals, in someembodiments it would be beneficial the two-kilohertz signal can bepre-processed and down sampled to reduce data transfer time withoutcompromising analytical power. This approach to down-samplingsignificantly reducing the size of and time to transfer data files fromthe DAU 110 to an external computer system 390 for analysis. Inalternative embodiments, a lower-powered micro-controller is used whenthe DAU is used as a recorder. The micro-controller and also includefeatures such as a temperature monitor, analog to digital converter,and/or the capability to transfer the data file in USB format to reducethe need for extra components.

FIG. 4 is a block diagram illustrating an example computer system 400that may be used in connection with various embodiments describedherein, according to an embodiment. For example, FIG. 4 illustrates anexemplary computer system that can be used in conjunction the DAU 110according to an embodiment. In some embodiments, the computer system 400may be implemented as external computer system 390 in, for example, anICU, emergency room, hospital environments, out-patient environments, auser's home, or etc. In some embodiments, the external computer system390 is a medical personnel's computer system 560 or mobile device 550.For example, medical personnel wishing to perform a sleep assessment ona patient can issue a DAU 110 to the patient. The DAU 110 can be used inany desired environment local to or remote from the medical personnel tocapture sleep related data and return the DAU 110 to the medicalpersonnel who can then download the data from the DAU 110 in order toassess the sleep quality of the patient. The system 400 may be used asor in conjunction with or as components of one or more of themechanisms, processes, or devices described elsewhere herein, includingthose components illustrated in FIG. 6 below. As will be clear to thoseskilled in the art, alternative processor-enabled systems and/orarchitectures may also be used.

In addition, the computer system 400 may support or implement any otherconventional or future method of user interaction. Such methods mayinclude augmented reality (e.g., overlaying any of the visual elementsdescribed herein over a real-time image of the user's physicalenvironment), virtual reality (e.g., providing a virtual universe inwhich the user can move and with which the user can interact usingconventional virtual reality gear, such as a headset, hand paddles,etc.), and/or the like.

The system 400 preferably includes one or more processors, such asprocessor 410. Additional processors may be provided, such as anauxiliary processor to manage input/output, an auxiliary processor toperform floating point mathematical operations, a special-purposemicroprocessor having an architecture suitable for fast execution ofsignal processing algorithms (e.g., digital signal processor), a slaveprocessor subordinate to the main processing system (e.g., back-endprocessor), an additional microprocessor or controller for dual ormultiple processor systems, or a coprocessor. Such auxiliary processorsmay be discrete processors or may be integrated with the processor 410.Examples of processors which may be used with system 400 include,without limitation, the Pentium® processor, Core i7® processor, andXeon® processor, all of which are available from Intel Corporation ofSanta Clara, Calif.

The processor 410 is preferably connected to a communication bus 405.The communication bus 405 may include a data channel for facilitatinginformation transfer between storage and other peripheral components ofthe system 400. The communication bus 405 further may provide a set ofsignals used for communication with the processor 410, including a databus, address bus, and control bus (not shown). The communication bus 405may comprise any standard or non-standard bus architecture such as, forexample, bus architectures compliant with industry standard architecture(ISA), extended industry standard architecture (EISA), Micro ChannelArchitecture (MCA), peripheral component interconnect (PCI) local bus,or standards promulgated by the Institute of Electrical and ElectronicsEngineers (IEEE) including IEEE 488 general-purpose interface bus(GPIB), IEEE 696/S-100, and the like.

System 400 preferably includes storage devices, such as, a main memory415 and an optional secondary memory 420. The main memory 415 providesstorage of instructions and data for programs executing on the processor410, such as one or more of the functions and/or methods discussedabove. It should be understood that programs stored in the memory andexecuted by processor 410 may be written and/or compiled according toany suitable language, including without limitation C/C++, Java,JavaScript, Perl, Visual Basic, .NET, and the like. The main memory 415is typically semiconductor-based memory such as dynamic random accessmemory (DRAM) and/or static random access memory (SRAM). Othersemiconductor-based memory types include, for example, synchronousdynamic random access memory (SDRAM), Rambus dynamic random accessmemory (RDRAM), ferroelectric random access memory (FRAM), and the like,including read only memory (ROM).

The secondary memory 420 may optionally include an internal memory 425and/or a removable medium 430, for example a floppy disk drive, amagnetic tape drive, a compact disc (CD) drive, a digital versatile disc(DVD) drive, other optical drive, a flash memory drive, etc. Theremovable medium 430 is read from and/or written to in a well-knownmanner. Removable storage medium 430 may be, for example, a floppy disk,magnetic tape, CD, DVD, SD card, etc.

The removable storage medium 430 is a non-transitory computer-readablemedium having stored thereon computer executable code (i.e., software)and/or data. The computer software or data stored on the removablestorage medium 430 is read into the system 400 for execution by theprocessor 410.

In alternative embodiments, secondary memory 420 may include othersimilar means for allowing computer programs or other data orinstructions to be loaded into the system 400. Such means may include,for example, an external storage medium 445 and an interface 440.Examples of external storage medium 445 may include an external harddisk drive or an external optical drive, or and external magneto-opticaldrive. External storage medium 445 may also be cloud storage.

Other examples of secondary memory 420 may include semiconductor-basedmemory such as programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasable read-onlymemory (EEPROM), or flash memory (block oriented memory similar toEEPROM). Also included are any other removable storage media 430 andcommunication interface 440 which allow software and data to betransferred from an external medium 445 to the system 400.

According an embodiment, the main memory 415 and/or secondary memory 420may comprise a patient data store, a reporting data store, a diseasemanagement recommendations (e.g., recommendations and/or interventions)data store, and a comparative data data store. In an embodiment, thedata stores can be relational databases or other types of persistent andsearchable data stores in memory 415 of computer system 400. Accordingto some embodiments, one or more of the data stores can be stored on anexternal server and can be accessed by external computer system 390 viaa network connection.

The patient data store may store patient related data, e.g. a patientidentifier and/or patient demographic information. Patient data storemay also include information of related ailments, diseases, etc.indicative of the acute status of the patient. The patient data storemay also include modified sleep staging rules as described in moredetail below in connection to FIG. 12. Patient data from the patientdata store can be used in the various assessments described herein forassessing the sleep quality of the user. The reporting data store can beused to store generated reports and can also include report templatesthat can be used to determine the format of the report and/or the typesof analysis to be included in the reports. The disease managementrecommendations data store can be used to store various treatmentrecommendations and/or intervention parameters that can be included inpatient reports based on the analysis of the data gathered by the DAU110 and used to make adjustments to the patient's care. The comparativedata data store can be used to store comparative data from healthypatients and/or patients with a chronic illness that causes sleepquality to degrade. The comparative patient data can be used, in part,to assess the sleep quality of a patient by providing a baseline ofhealthy and ill patients against which a user's data can be compared.

System 400 may include a communication interface 440. The communicationinterface 440 allows software and data to be transferred between system400 and external devices (e.g. printers), networks, displays, orinformation sources. For example, computer software or executable codemay be transferred to system 400 from a network server via communicationinterface 440. Examples of communication interface 440 include abuilt-in network adapter, network interface card (NIC), PersonalComputer Memory Card International Association (PCMCIA) network card,card bus network adapter, wireless network adapter, Universal Serial Bus(USB) network adapter, modem, a network interface card (NIC), a wirelessdata card, a communications port, an infrared interface, an IEEE 1394fire-wire, or any other device capable of system 400 with a network oranother computing device.

Communication interface 440 preferably implements industry promulgatedprotocol standards, such as Ethernet IEEE 802 standards, Fiber Channel,digital subscriber line (DSL), asynchronous digital subscriber line(ADSL), frame relay, asynchronous transfer mode (ATM), integrateddigital services network (ISDN), personal communications services (PCS),transmission control protocol/Internet protocol (TCP/IP), serial lineInternet protocol/point to point protocol (SLIP/PPP), and so on, but mayalso implement customized or non-standard interface protocols as well.

Software and data transferred via communication interface 440 aregenerally in the form of electrical communication signals 455. Thesesignals 455 are preferably provided to communication interface 440 via acommunication channel 450. In one embodiment, the communication channel450 may be a wired or wireless network, or any variety of othercommunication links. Communication channel 450 carries signals 455 andcan be implemented using a variety of wired or wireless communicationmeans including wire or cable, fiber optics, conventional phone line,cellular phone link, wireless data communication link, radio frequency(“RF”) link, or infrared link, just to name a few.

Computer executable code (i.e., computer programs or software) is storedin the main memory 415 and/or the secondary memory 420. Computerprograms can also be received via communication interface 440 and storedin the main memory 415 and/or the secondary memory 420. Such computerprograms, when executed, enable the system 400 to perform the variousfunctions of the present invention as previously described.

In this description, the term “computer readable medium” is used torefer to any non-transitory computer readable storage media used toprovide computer executable code (e.g., software and computer programs)to the system 400. Examples of these media include main memory 415,secondary memory 420 (including internal memory 425, removable medium430, and external storage medium 445), and any peripheral devicecommunicatively coupled with communication interface 440 (including anetwork information server or other network device). Thesenon-transitory computer readable mediums are means for providingexecutable code, programming instructions, and software to the system400.

In an embodiment that is implemented using software, the software may bestored on a computer readable medium and loaded into the system 400 byway of removable medium 430, I/O interface 435, or communicationinterface 440. In such an embodiment, the software is loaded into thesystem 400 in the form of electrical communication signals. Thesoftware, when executed by the processor 410, preferably causes theprocessor 410 to perform the inventive features and functions previouslydescribed herein.

In an embodiment, I/O interface 435 provides an interface between one ormore components of system 400 and one or more input and/or outputdevices. Example input devices include, without limitation, keyboards,touch screens or other touch-sensitive devices, biometric sensingdevices, computer mice, trackballs, pen-based pointing devices, and thelike. Examples of output devices include, without limitation, cathoderay tubes (CRTs), plasma displays, light-emitting diode (LED) displays,liquid crystal displays (LCDs), printers, vacuum florescent displays(VFDs), surface-conduction electron-emitter displays (SEDs), fieldemission displays (FEDs), and the like.

The system 400 also includes optional wireless communication componentsthat facilitate wireless communication over a voice and over a datanetwork. The wireless communication components comprise an antennasystem 470, a radio system 465 and a baseband system 460. In the system400, radio frequency (RF) signals are transmitted and received over theair by the antenna system 470 under the management of the radio system465.

In one embodiment, the antenna system 470 may comprise one or moreantennae and one or more multiplexors (not shown) that perform aswitching function to provide the antenna system 470 with transmit andreceive signal paths. In the receive path, received RF signals can becoupled from a multiplexor to a low noise amplifier (not shown) thatamplifies the received RF signal and sends the amplified signal to theradio system 465.

In alternative embodiments, the radio system 465 may comprise one ormore radios that are configured to communicate over various frequencies.In one embodiment, the radio system 465 may combine a demodulator (notshown) and modulator (not shown) in one integrated circuit (IC). Thedemodulator and modulator can also be separate components. In theincoming path, the demodulator strips away the RF carrier signal leavinga baseband receive audio signal, which is sent from the radio system 465to the baseband system 460.

The baseband system 460 is also communicatively coupled with theprocessor 410. The processor 410 has access to data storage areas 415and 420. The processor 410 is preferably configured to executeinstructions (i.e., computer programs or software) that can be stored inthe memory 415 or the secondary memory 420. Computer programs can alsobe received from the baseband processor 460 and stored in the datastorage area 415 or in secondary memory 420, or executed upon receipt.Such computer programs, when executed, enable the system 400 to performthe various functions of the present invention as previously described.For example, data storage areas 415 may include various software modules(not shown).

According to an embodiment, DAU 110 can be configured to perform variousprocesses on the data collected from the sensors and to download theprocessed data to computer system 400. According to some embodiments,the DAU 110 can capture and store data from the various sensors and thedata is downloaded to computer system 400 for processing. As describedabove, the DAU 110 can include firmware that performs at least a portionof the processing of the signal data collected before the data isdownloaded to the computer system 400.

According to an embodiment, the computer system 400 can be used to viewdata (e.g., via a display connected at I/O interface 435) collectedand/or analyzed by DAU 110 and/or perform analysis and processing on thecollected data. According to an embodiment, the computer system 400 canalso generate reports based on the data collected by the DAU 110. Invarious embodiments, the computer system 400 may perform actions (e.g.,interventions, feedback, stimulus, etc.) based on the analyzed data tocontrol and/or steer the patient into a desired sleep state and/or awayfrom or out of undesired sleep states, as described below in connectionto FIGS. 6-28.

According to an embodiment, the DAU 110 can include software fordownloading data captured by the DAU 110 and/or the sensors interfacedwith the DAU 110 to a remote computer system (e.g., computer systems390, 560 or mobile device 550 described below in connection with FIG. 5)via a network and/or cloud server 540. For example, in an embodiment,the DAU 110 can include software that periodically connects to externalcomputer system 390 via a wireless interface, downloads data from theDAU 110 to the external computer system 390, and triggers a transfer ofthe data from the external computer system 390 to a remote computersystem 560, such as a doctor's computer system or a web portal. In anembodiment, the remote computer system can be a web portal comprisingone more remote servers that can collect and analyze data received fromDAU units. For example, a doctor treating a patient can create anaccount on the web portal for that patient and associate the accountwith a particular DAU 110. The patent can then use the DAU 110 tocapture data

DAU 110 may perform one or more of the steps in the various processesdescribed herein, including one or more of the steps in the processesillustrated in FIGS. 6-28 described below. In addition, externalcomputer system 390 may perform other ones of the steps in theseprocesses. For example, DAU 110 may perform steps corresponding to thecollection of data, signal processing, etc., whereas external computersystem 390 may perform steps corresponding to the analysis of thecollected data (e.g., calculation of metrics, etc., based on thecollected data) and the display of graphical user interfaces, reports,etc. related to the analysis (e.g., visual results of the analysis).

In some embodiments, DAU 110 can be integrated with one or more wirelesssensors for measuring various physiological data that can be used toidentify sleep disruptions. For example, sensor of the DAU 110 maycomprises wireless sensors used to measure pulse/oximetry from thefinger, a device that obtains electro-cardiographic signals (e.g.,holter monitor), respiratory effort belt, and transducer to measure limbmovements. However, in other embodiments other types of sensors formeasuring physiological signal data can be used and differentcombinations of sensors can be used. The data from these sensors can beused to collected data used by the DAU 110 in the concurrent measurementof signal data related to sleep architecture and of signal data relatedto sleep disruptions.

As described above, DAU 110 can include a wireless transmitter/receiver377 incorporated into the data transfer module 326 to receive data fromperipheral sensors (i.e., wireless ECG sensors, finger pulse oximeter,respiratory effort bands, sensors measuring leg movements, etc.) and/ortransmit signals to an external computer system 390 for real timemonitoring of the data being acquired by the DAU 110. Data acquired fromthese sensors can be used to determine the user's sleep architectureand/or to identify sleep disruptions that can negatively impact sleepquality.

According to an embodiment, each of these wireless sensor sub-systemscan have a separate power supply and data storage. The DAU 110 and thewireless sensor sub-systems can be integrated to align the data from thesensor sub-systems with the data generated by the DAU 110. For example,the data can be aligned by using a common time stamp on all data thatcan be used to determine when data was recorded by the DAU 110 and/orthe sensor sub-systems. According to an embodiment, this integration canbe achieved by configuring the DAU 110 or one of the sensor sub-systemsto operate to serve as a master device that wirelessly transmits a timestamp that is received by the other integrated components of the system.Each of the components of the system can include a wireless receiver forreceiving the timestamp information and be configured to use thetimestamp information transmitted by the master device to synchronize aninternal clock to that of the master device or to use the timestampinformation transmitted from the master device to timestamp datagenerated by the receiving device. According to an alternativeembodiment, the sensor sub-systems can be integrated with the DAU 110 bycoupling the DAU 110 to the sensor sub-systems using a wire. In such awired configuration, the DAU 110 and the sensor sub-systems can operateusing a common power supply and use common data storage.

In an embodiment, central sympathetic arousals or variability insympathetic activation can be measured with two dry electrodes (i.e.,capable of acquiring the ECG signal through clothes). One benefit ofrecording ECG is to more accurately identify cardiac problems (e.g.,cardiac dysrhythmia, etc.). Alternatively, sympathetic arousals can bedetected with a pulse signal or peripheral arterial tone signal. Thepulse signal can be obtained using a sensor located at the user'sforehead or any other location (e.g., ear, finger, etc.) which obtainscapillary blood flow and is appropriate for either reflectance ortransmittance methodologies/technology.

According to an embodiment, electro-neuro-cardio-respiratory sensorsused to assess sleep quality can be incorporated into the sensor strap120. As described above, the sensor strap 120 can be removeably coupledto the DAU 110 via a socket connection on the DAU 110 that electricallycouples traces included in the sensor strap 120 with the DAU 110.

As shown in FIG. 2A, the sensor strap 120 can be used to acquirephysiological signals that can be used in the concurrent measurementsrelated to sleep architecture and sleep disruptions that is performed bythe DAU 110 according to an embodiment. The sensor strap 120 includestraces that create electrical circuit connections while holding thesensors against the user's forehead. Within the sensor strap 120,EEG/EOG/EMG electrodes 235 are optimally positioned to measure rapid eyemovements, cortical arousals, sleep spindles, K-complexes and stagesleep. Sensors placed on the forehead may be capable of acquiring boththe brain's electrical activity and eye movements. In one embodiment,the sensor strap 120 provides for at least one sensor to be placed offthe forehead in a non-frontal region of the brain to improve thedetection of alpha waves which are used to assess sleep onset andcortical arousals. According to an embodiment, the sensor strap 120 alsoprovides the electrical pathway to drive the red and infrared lightemitting diodes and photodiodes in the reflectance sensor 230. Thereflectance sensor 230 can be used to generate signals for thecalculation of oxyhemoglobin saturation and pulse rate of the user. Fromthe reflectance sensor 230 inputs, a photoplethesmographic signal can bederived to measure respiratory effort via changes in forehead venouspressure.

In an embodiment, the number of sensors included in the sensor strap 120is minimized and the connection between the sensors in the sensor strap120 and the DAU 110 is a wireless connection. As a result, the sensorstrap 120 can be configured for use on numerous sites, using varioussensor combinations, and can be used with user's having different headsizes. In an embodiment, additional EEG sensors (e.g., electrodes) orconnectors can be added to the sensor strap 120 to create the flexibleinterface to the electronic circuitry.

Furthermore, in some embodiments, inter-electrode spacing can beadjusted to accommodate adolescent and child head sizes. In someembodiments, headband 130 can be integrated into or affixed over thesensor strap 120 to increase ease of preparation. Rather than usingindividual EEG electrodes 235 and a comfort strip, the sensor strap 120may comprise a sheet of adhesive foam in which the sensors are embeddedand with conductive gel placed over the conductive sensors. The use offoam or alternative potting method ensures the light from thereflectance sensor is transmitted into the skin and not directly to thephotodiode.

FIG. 5 schematically illustrates an integrated system for monitoringphysiological signal patterns of one or more patients, in accordancewith various embodiments. The integrated system of FIG. 5 comprises aDAU 110 communicatively coupled to an external computer system 390 via acommunication interface 505 (e.g., a wired or wireless communicationconnection). The external computer system 390 may be communicativelycoupled to a network and/or cloud server 540 (e.g., through a wired orwireless communication connection). FIG. 5 also illustrates mobiledevices 550 and or other computer systems 560 that are coupled to andmay access the cloud server 540 via a network and/or other wired orwireless communication connections. While FIG. 5 illustrates one of eachof DAU 110, computers systems 390, 560, and mobile device 550, it willbe appreciated that any number of DAUs may be connected to a singlecomputer system 390 or multiple computer systems. Thus, in someembodiments, a plurality of DAUs may be communicatively coupled to oneor more computer systems which may communicate patient data to the cloudserver 540.

FIG. 5 illustrates remote, mobile and/or online access to patientphysiological data. FIG. 5 depicts various devices for analysis,communication, and processing data from DAU 110, for example, computersystems 390, 560 and/or mobile device 550, according to variousembodiments. Each of these devices may comprise one or more processorsfor executing instructions stored in a memory. Each device maycorrespond to a desktop computer, laptop computer, tablet, mobiledevice, or other apparatus comprising at least a processor, memory, anda display. In the illustrated embodiment, these device may comprise anacoustic microphone, a micro-controller, an audio output, a power sourcecomponent (e.g., comprising a power supply and/or battery), a datatransfer module (e.g., comprising a data storage medium and/or datatransfer interface), wired network connection (e.g., a Ethernet port,telephone port, etc.), and/or a wireless transmitter, receiver, ortransceiver (e.g., Bluetooth, Wi-Fi, ZigBee, 3G, 4G, 5G, LTE, RFID, NFC,etc.). In addition, computer system 390 may be communicatively connectedto the DAU 110, cloud server 540, mobile device 550, and/or computersystem 560 (e.g., via wired and/or wireless network connections) andvice versa. It should be understood that, in alternative embodiments,each device may have fewer, more, or different components, as well as adifferent arrangement of components.

In some embodiments, the system of FIG. 5 also may include a cloudserver 540 in communication with one or more of the computer system 390,computer system 560, mobile device 550, and/or DAU 110. In someembodiments, the cloud server 540 may be part of a cloud computingarchitecture comprising servers (including processors for executinginstructions) and data storage devices communicatively connected tomobile device (e.g., mobile device 550) and/or computers (e.g., computersystems 390 and 560) via a network (e.g., Internet, Intranet, etc.). Insome embodiments, the connected device may comprise a client installedthereon for accessing and interacting with the cloud server 540. Thecloud server 540 may perform steps corresponding to the analysis of thecollected data and remotely store data from any one of the connecteddevices.

In some implementations of FIG. 5, the integrated system may provide forsharing of the patient's signal patterns. In some embodiments, sharingof the patient's signal patterns maybe done over a secure and/or privatecommunication protocol. In an example embodiment, signals acquired withthe DAU 110 can be transmitted via wired or communication interface 505to a computer system 390 for processing and/or presentation. In variousembodiments, the signals may be transferred using wireless technology(e.g., Bluetooth, Wi-Fi, LAN, etc.) to permit the DAU 110 and thecomputer system 390 to be physically untethered. As a result, thepatient could be free to move. Wireless transmission may also provideusers of the system the option to move the computer system 390, forexample, outside the patient's room or about the environment, whilestill monitoring the signals from the DAU 110. In various embodiments,the wireless technology may permit centralized monitoring of multiplepatients via multiple DAUs 110. Each of the multiple patients may belocated in one or more different rooms and each may include a DAU 110affixed to the patient. For example, the wireless transmission maypermit a healthcare worker to monitor up to six patients from a centralcomputer or site. Additionally, for example, the wireless transmissionmay permit a healthcare worker to monitor up to six separate rooms, eachroom occupied by one or more patients. In one embodiment, the computersystem 390 can be affixed to a stand inside the patient's room. In oneembodiment, the stand has a locking enclosure to secure the computer.While affixed to inside a first patient's room, the computer system 390may be able to monitor additional patients located in other rooms viawireless communication. In any one of the embodiments described hereinor in an alternative embodiment, the computer may be part of anintegrated patient monitoring system, such as for example, a vital signmonitor or mechanical ventilator. Other monitoring systems are possible.

Various implementations of the systems described herein provide for themonitoring of physiological signals for detection of abnormal signalpatterns and/or conditions (collectively referred to as “conditions”)that may occur in patients. In some implementations, a patient may be inan intensive care unit (ICU) and the monitored condition may be at leastin part a result of their acute status. For example, a DAU 110 may beaffixed to a patient's forehead that is in the ICU and the computersystem 390 may be disposed within the ICU or communicatively coupled tothe DAU 110. The computer system 390 and/or other devices of FIG. 5 mayreceive physiological signals from the DAU to either display and/oridentify single patterns. Example signal patterns and conditions aredescribed below in connection to FIGS. 8-28. These conditions, however,may also be present in patients being admitted to the hospital oremergency room, and/or patients being nursed on the hospital floor.Thus, in various embodiments described herein, the absence and/orpresence of one or more of these conditions can be useful to assist inidentifying patients who could be transferred from the ICU to a stepdownunit, or identifying those in an acute state that requires increasedmonitoring or emergency care, e.g., typically delivered in the ICU.While certain example implementations of the systems of FIG. 5 aredescribed within a hospital setting, it will be appreciated that thesystems and methods herein are not to be limited to only theseapplications. The DAU 110 and/or integrated system of FIG. 5 may beimplemented in any environment whereby a physiological signals of apatient may be collected and analyzed to improve sleep quality.

In some embodiments, wireless or wired acquisition, digitization andtransmission of the physiological signals may reduce the likelihood ofartifact contamination of neurophysiological signals. The capability toinspect EEG signals, and associate EEG with position, sound, andmovement when abnormal power spectra are detected permits a user tofurther differentiate true neurological patterns from signal noiseand/or interference. Once recognized, the impact of the interactionbetween a change in medication (e.g., dose amount and/or type ofmedication) and abnormal signal patterns can be assessed.

In various implementations, signal patterns detected by, for example,the DAU 110 may assist with recognizing the presence of conditions. Thedetected physiological signal patterns may be used by users (e.g.,medical personnel) to improve or modify patient care. For example, insome embodiments, access to a visual display or other notificationdevice (e.g., device outputting sound, vibrations, tactile feedback,light, etc.) of the detected signal patterns may be useful inrecognizing and determining the presence of abnormal neurophysiologicalpatterns. Users may then identify a physician and/or specialist who maybe needed for consultation based, in part, on the presence of adetermined abnormal pattern. For example, if abnormal neurologicalpatterns are observed via the notifications or visual display (e.g.,seizure, burst suppression, sepsis associated encephalopathy patterns,etc.), a neurologist may be consulted to recommend the type and dosageof anti-seizure mediation. As another example, alone or in combination,a pulmonologist may be consulted to help stabilize the mechanicalventilator if patterns of ventilatory distress are recognized. Eitherspecialist may recognize patterns that suggest the need for modificationto the type and dosage of a sedative. Access to the visual display ofthe signal patterns of the patient may assist specialists to provideguidance without necessarily having to be present at the patient'sbedside or in the same building. While the embodiments herein aredescribed in connection to a visual display, other forms of notifying aphysician of the presence or absence of a condition may be used. Forexample, an auditory signals, tactile signals, etc. generated via acomputer and/or mobile device in response to a detected abnormal signalpattern.

One non-limiting advantage of the systems and methods described hereinis a capability to acquire and view physiological signal characteristicslocally (e.g., in a hospital room), as well as simultaneously view thesignal information remotely (e.g., by a user). In one embodiment, theDAU 110 may transmit the signals wirelessly (or over a wired connection)to a computer system 390 (or computer system 560 or mobile device 550).A user may be able to review the data signals using a graphical userinterface (GUI), for example, as described in connection to FIG. 29-31below. In another embodiment, the detected physiological signals may besimultaneously saved to the DAU 110 or external computer system 390 forsubsequent download and transfer to a web-portal, where any user withauthorized access can view the signals off-line from any location withinternet access. This approach may be beneficial for in-depth analysiswith interpretation for inclusion in the patient's medical record. Thisembodiment can be further enhanced with updates (e.g., periodically,intermittently, or on demand by a user) of the signals and process asthe data is acquired. This may enable off line viewing of the patient'sup to date or approximately current physiological information. Suchembodiments may be beneficial to an expert who wishes to review data ofan at-risk patient while enabling access to long periods of previouslycollected data, as well as accessing the current information. For oneapplication, the expert may wish to compare the current data to aprevious period of recorded data, for example, data collected while thepatient is intubated and mechanically ventilated compared with datacollected while extubated and not mechanically ventilated.

In another embodiment, the signals are transferred from the computersystem 390 to a dedicated IP address or cloud server 540 where an expertcan review an image of the collected signals streamed from the cloudserver 540 to a computer system 560 or mobile device 550 in real time.In an alternative approach, software that characterizes and presents thesignals locally may also be applied for remote real time viewing of thesignal characterization described herewith. Either of the later twoapproaches will be optimal for an expert who is monitoring forepileptiform activity in real time.

When it is determined that a specialist needs to review the patient'ssignal patterns in accordance with the embodiments disclosed herein,such review could be conducted without the specialist having tophysically interact with the computer system 390. In one embodiment, thecomputer system 390 may acquire and/or generate study files comprisingpatient data received from the DAU 110. The patient data, and signalstherein, can be transferred to the cloud server 540 via a wired orwireless connection. In some embodiments the transfer may be done usingWi-Fi or other wireless communication protocol. The Wi-Fi connection maybe a secure connection in some embodiments. In another embodiment, aloneor in combination, the computer system 390 is connected to a local areanetwork that provides internet access to the cloud server 540. The cloudserver 540 may comprise a one or more processors coupled to a storagedevice, data store, or database for storing the patient data remote fromthe DAU 110 and/or computer system 390. The cloud server 540 may beoperating over the internet or an intranet. Thus, the cloud server 540may be a local cloud server 540 for operations within a given hospitalor location.

The storage device of the cloud server 540 may store patient data thatcan be reviewed by personnel authorized to access the cloud. Forexample, access to the cloud server 540 may be restricted to physiciansor caregivers employed by the hospital or any subset of persons havingaccess to the patient. In some embodiment, access to the cloud server540 may be done via a mobile device 550 (e.g., tablet or smart phone) ora computer system 560. For example, an authorized user may access thecloud server 540 via a cloud client installed on the mobile device 550and/or computer system 560 by entering verified credentials (e.g.,password, biometrics, etc.) that are capable of authenticating a user.In another example, the mobile device 550 and/or computer system 560 maybe authorized for access to cloud server 540, in which case the user maynot need to enter authorization information. Use of the mobile device550 would be useful in certain situations, for example, where thepersonnel reviewing the patient data is on call and away from thepatient. A detected abnormal signal pattern or condition could triggeran intervention notification that is transmitted to the on callpersonnel that may be received on, e.g., mobile device 550 regardless ofphysical location (as will be described in greater detail below inconnection to FIGS. 6 and 7). Presentation of the signal patterns on alarge screen, for example, as may be included with computer system 560may be useful when the reviewer is making annotations or edits, andentering report comments. In various embodiments, the signal data fromDAU 110 and/or from the computer system 390 can be transmitted to thecloud server 540 at periodic intervals (e.g., 3 or 5 minutes). Thisapproach may provide remote replication of what the healthcare worker isviewing on the patient's computer system 390. In some embodiments,computer system 560 may be computer system 390 or a separate computersystem.

In some embodiments, signals that have not been processed by the DAU 110and/or computer system 390 can also be transferred to the cloud server540 at the end of the monitoring session (e.g., as raw data). In someembodiments, a reviewer may then access the unprocessed signals viacomputer system 560 or mobile device 550 for subsequent processing andreviewing as described throughout this application. In some embodiments,alone or in combination, the cloud server 540 may include softwarecomprising instructions that cause a processor to analyze unprocessedsignals and/or reanalyze the signals for subsequent review. The cloudserver 540 may also be able to apply additional algorithms, e.g., forthe detection of seizure activity, prior to interpretation and editing.One non-limiting advantage of applying signal processing routines to theentire record stored on the cloud servers 540 is an ability to providemore sophisticated and complicated processing routines using additionalcomputing resources. Whereas other implementations may process a subsetof the data or process the data in real-time using limited computingresources thereby requiring less complicated algorithms to do so. Whilethis may permit real-time monitoring, such advantage is a trade off withlimited computing resources of the mobile device 550 and/or computersystem 560.

For example, FIG. 6 illustrates a flowchart for an example process 600of detecting abnormal signal patterns in patients, according to anembodiment. Process 600 may be implemented by the integrated system ofFIG. 5 described herein. The process 600 may be implemented to detectabnormal signal patterns or conditions as described herein for review byusers and recommending for managing the patient sleep based on or inresponse to the detected condition.

At step 610, the process detects an abnormal signal pattern. In oneembodiment, an abnormal pattern may be detected via DAU 110, forexample, by a healthcare worker trained to use the computer system 390to detect the abnormal patterns that require a specialist review. Insome embodiments, automated algorithms can be employed to detect any oneor more of or all of the conditions described in FIGS. 8-28 (e.g.,through a comparison of power spectra waveforms representative of thephysiological signals). Automated algorithms for detecting theseconditions may reduce the occurrence that a user does not recognize whenintervention is required and/or patient data should be reviewed by aspecialist (e.g., burst suppression, sepsis associated encephalopathy,respiratory distress, etc. as described herein) for possiblemodification of treatment. Thus, in some embodiments, a notification(e.g., a message) or alarm may be initiated at step 620 via the DAU 110,computer system 390 or other external system. The alarm may be audible(e.g., a sound or noise indicting a detected condition), tactilefeedback, visual (e.g., a light that is flashed or otherwise strobed),or a message sent via one or more of the computer systems 390 to amobile device or other computer system (e.g., systems 550 and/or 560).

To avoid and/or minimize sounding false alarms, in some embodiments,various thresholds can be applied to each of the automatedalgorithm/detection rules. Each of the thresholds may be adjustable. Forexample, sleep is very important for the patient's recovery, thus athreshold may be based on the amount of sleep (e.g., amount of timespent sleeping). For example, an intervention notification or alarmcould notify the hospital staff when the patient has had too littlesleep (e.g., less than 6 hours in the previous 24 hours). The decisionon whether to apply a threshold for 6, 7 or 8 hours of sleep, forexample, may be dependent on the type and dose of medication beingadministered or other external factors. Other example thresholds may bebased on the acute status or circumstance of the patient to avoid falsealarms. For example, ambient noise thresholds may be based on themagnitude and duration of the ambient noise that would interrupt sleep,which may differ based on time of day and associated during circadiandips. For example, a hospital environment at one time during the day mayhave more ambient noise than a later time at night. An alarm would beappropriate with detection of loud sound coupled with an awake conditionduring the sleeping portion of the circadian rhythm. Conversely, a loudsound coupled with sleep may indicate sleep disordered breathing. In oneembodiment, the sounding of an alarm could trigger a bedside review ofthe previously recorded study data to rule out the presence of abnormalphysiological patterns (e.g., three or more consecutive minutes ofabnormal slow wave activity as described below).

At step 630, recommendation information may be optionally presented forspecific conditions. For example, recommendation information may bepresented to a caregiver via computer system 390, mobile device 550,and/or computer system 560 for providing interventions with thepatient's care. In some embodiments, the recommendation information maybe stored locally on a device and/or stored in the cloud server 540 andtransmitted to a device operated by the caregiver, for example, in thedisease management recommendation data store described above. Therecommendation information may comprise interventions designed toconsolidate and/or manage sleep patterns of the patient. Theseinterventions can be standardized and developed by, for example, keyopinion leader(s), committee of hospital staff members, or consensusopinion of a professional society. In some embodiments, recommendationinformation may be stored and mapped with conditions, such that when aspecific condition is identified the recommendation (and associatedintervention) may be retrieved. Furthermore, the patient information inthe patient data stored can be accessed, compared to information in thecomparative data data store to identify differences between the patientin question and a health patient, and this comparison may be associatedwith a specific condition used to retrieve the recommendationinformation.

A graphical user interface installed and operated, for example, on themobile device 550, computer system 390, and/or computer system 560 canbe used by the caregiver to set or adjust alarm settings to theirspecifications based in part on the intervention recommendations. Forexample, if non-convulsive seizure activity is detected in the signalpatterns, an example recommendation may be to intervene by placing thepatient on anti-convulsive medications and initiate continuous EEGmonitoring. Another example recommendation may also include a care giverintervening by reviewing and/or changing the type or dosage ofadministered medication(s) e.g., sedative.

At step 640, an optimal notification may be delivered to the designateduser (e.g., specialist(s). The presentation of the notification may besimilar to the notification of step 620. For example, a notification maybe automatically delivered via the computer system 390 or DAU 110dependent on the condition detected by the DAU 110. The condition may berecognized by processing of the signals from the DAU 110 as describedherein. The specialist may then use, for example, the system of FIG. 5to review the signal data via, for example, a GUI (e.g., FIGS. 29-31) toidentify and analyze the data for abnormal signal patterns.

In various embodiments, the specialist can use a mobile device 550 orcomputer system 560 to input confirm or other provide recommendations,instruct caregivers to intervene in accordance with the recommendations,and/or modify the recommendations (step 650). In some embodiments, thespecialist may be able to instruct systems and device surrounding thepatient to intervene, for example, modify a dosage and/or rate ofmedication supplied by an IV. Other interventions may be readilyapparent in a hospital setting for remote control. Such inputs may betransmitted to the cloud server 540 for storage. Additionally, using thecloud server 540, the recommendations may be transmitted to the patientcomputer system 390 for presentation to the patient's healthcare worker,at step 660, and/or automated action by medical devices associated withthe patient's care. One skilled in the art will recognize that thesesteps could be implemented in part, in total, or in any order. In someembodiments, the delivery of notifications and communication between thehealthcare worker and the specialist, with respect to interventionrecommendations, can be made via telephone, email, or other means ofcommunication.

Alternatively, the integrated system of FIG. 5 may be automated, forexample, by removing the need for a healthcare giver at the computersystem 390. Accordingly, the systems and methods described herein mayprovide both diagnostic and therapeutic benefit. In some embodiments,the integrated system may be automated for example so to diagnosispatients without user intervention. For example, the DAU 110 may detectsignals representative of an abnormal signal pattern processed by thecomputer system 390 and/or cloud server 540, e.g., such as theconditions described in FIGS. 8-28. The detected condition may triggeran intervention message sent by the computer system 390 to retrieveintervention recommendations related to the condition, as describedabove. In some embodiments, the computer system 390 and/or cloud server540 may transmit a message to mobile device 550 and/or computer system560 to notify the designated specialist or physician of the presence orabsence of the condition for implementation of the recommendation. Thus,the need for a healthcare provider near the patient computer system 390may be reduced or removed. In some embodiment, the computer 390 mayselect one or more recommendations, for example, by identifying,notifying, or displaying studies to a clinician that meet his/her or thehospital's designated criteria for review. Additionally oralternatively, in certain situations, the computer system 390 may, basedon the patient's status, medical needs, and detected condition, retrieveand execute the recommendation without intervention by a healthcaregiver or designated physician. Thus, the integrated system may be fullyautomated and reactive to the detected conditions in real-time.

For example, returning to FIG. 5, the integrated system may alsocomprise an optional sleep guidance system 530 coupled to the DAU 110and/or computer system 390. For example, the DAU 110 (or othercomponents of the integrated system of FIG. 5) may detect signalsrepresentative of the conditions describe herein and process the signalsto trigger the above described recommendation and intervention messages.The intervention message may be transmitted directly or indirectly(e.g., from a specialist following the steps of FIG. 6) to the sleepguidance system 530. The sleep guidance system 530 can be configured toinitiate a therapeutic action based on or in response to receiving theintervention message.

The sleep guidance system 530 may comprise one or more devices and/orsystems for controlling peripheral equipment connected to and providingfor the patient medical care (e.g., monitoring, administrating, and/orfacilitating the patient's medical needs). For example, the sleepguidance system 530 may comprise a device for controlling medicationprovided to the patient via an IV. The device may be able to controldosage, timing, and type of medication administered to the patient.Other medical equipment and devices controlled thereby are possible.

In one example, the sleep guidance system 530 may include a stimulusgenerator that controls one or more peripherals for execute thetherapeutic actions based on intervention recommendations (e.g., forstaging sleep and/or managing the patients sleep cycle through the sleepstages). Some example peripherals include, but are not limited to,devices to generate and control light, sound, temperature, and tactilefeedback based on the received signal. The DAU 110 may becommunicatively coupled (e.g., via wired or wireless communication) tothe sleep guidance system 530. In another embodiment, the sleep guidancesystem may be part of and/or embedded in a common device as the DAU 110.In some embodiments, alone or in combination with the above describedembodiments, the sleep guidance system 530 may be communicativelycoupled to the computer system 390, cloud server 540, computer system560, mobile-device 550, or any combination thereof.

In various embodiments, the sleep guidance system 530 may besubstantially similar to the systems and devices described in U.S. Pat.Nos. 8,628,462; 8,784,293; and 8,932,199, all of which are herebyincorporated by reference in their entirety. These patents describesystems and methods for optimization of sleep. For example, thephysiological signals of a patient may be monitored to identify acurrent sleep state experienced by a patient, determine a desired sleepstate that the patient should be experiencing based on sleeparchitecture data for the patient, identify sensory stimuli that may beapplied to the patient to guide the patient to the desired sleep statefrom the current sleep state, and generate the sensory stimuli to guidethe patient from the current sleep state to the desired sleep state.Such concepts may be applied to the present disclosure, for example,where guiding the patient to a desired sleep state (or away from anundesired sleep state) may be a form of therapeutic action. The sleeparchitecture data may be based, in part, on the methods and conditionsdescribed in connection to FIGS. 8-28. Continual monitoring ofphysiological signals as described herein of the patient allows thesystem to adapt to changes in the sleep state of the patient and toadjust the stimuli being generated based on interventionrecommendations. Embodiments also provide for detection and protectionof the patient from environmental disturbances, such as noise, light,and temperature changes. Thus, the patient may be guided to desiredsleep states to improve recovery time and managing administration ofmedication.

Furthermore, the systems and methods may be implemented to achieveefficient sleep periods of a patient even where there is little sleeptime available or when the sleep periods are interrupted, for example,due to ICU and/or other hectic environments, administering ofmedication, or aliments occurring during a sleep cycle as describedherein. Thus, embodiments herein can be used to optimize the sleepcycles of a patient to allow the patient to experience more efficientsleep, to wake feeling more refreshed, to require less sleep than thepatient may have required without the optimizations, and to reduce theimpact of medication and improve recovery time.

In some embodiments, the sleep guidance system 530 may be a sleep maskas described in the above identified patents. Alternatively, or inaddition, the sleep guidance system may be configured to controlperipherals in the surrounding ambient environment in which the patientis sleeping. The peripherals may be communicatively coupled to thecomputer system 390, DAU 110, and/or other systems that may provideautomated and/or commands for controlling such peripherals.

The sleep guidance system 530 may, for example, acquire and monitor oneor more physiological signals, indicative of a sleep state of a patient(e.g., as described below step 805 and 810 of FIG. 8). According to anembodiment, the physiological signals can include, but are not limitedto, electroencephalogram (EEG), electrooculogram (EOG), electromyogram(EMG), respiration, heart rate, body movement, galvanic skin reaction,blood pressure, blood flow, blood chemistry, behavioral responses, orsome combination thereof.

A current sleep state of the patient can be determined using thephysiological signals (e.g., as described below in step 815 of FIG. 8).According to an embodiment, the physiological signals may be processedusing a set of basic signal conditioning algorithms (e.g., artifactrecognition and rejection, band-pass filtering, and/or other signalconditioning algorithms). According to an embodiment, the sleep state ofthe patient may be determined using machine learning, patternrecognition, artificial intelligence, optical character recognition, orsimilar techniques to match the physiological signals obtained from thepatient with one of the sleep stages as described herein.

The current sleep state information for the patient may then be added toa sleep state record associated with the patient. According to anembodiment, the sleep state record associated with the patient may bestored and include, for example, historical sleep data associated withaliments and abnormal sleep events. The sleep state record for thepatient may also include a record of recent sleep informationrepresenting the sleep architecture of several most recent sleepepisodes of the patient. The sleep architecture associated with thepatient may be updated with the current sleep state for the patient atthe end of each ongoing sleep episode.

A desired sleep state can then be determined by applying a set of rulesto the current sleep information and the recent sleep information. Therules aid in optimizing the sleep performance of the patient byidentifying a desired sleep state that the patient should beexperiencing at a particular time, for example, as described in moredetail in connection to FIGS. 8-28. A set of rules may be defined for aparticular patient and/or a particular set of sleeping parameters. Forexample, a set of rules may be defined for a patient who is in the ICUfor a given aliment, where irregular and abbreviated periods of sleepcan occur due to environmental, illness, and/or medication conditions.According to an embodiment, the personalization of the rules to suit theneeds of the particular sleeper can include evaluating whichphysiological characteristics most clearly indicate a change between thesleeper's sleep states, which patterns of physiological characteristicsoccur at which portions of the sleeper's sleep cycle or under whichcircumstances, how a sleeper's physiological characteristics or sleeppatterns change when exposed to sensory stimuli and/or medication, how asleeper's physiological characteristics respond when sleep is disrupted,optimal durations and patterns for a sleeper's sleep cycle, what sensorystimuli works most effectively to move the sleeper through the sleepstages, and/or other processes for calibrating the rules to the needs ofa particular patient. For example, as described below, certain sleepstates may be undesirable for certain aliments and thus one rule may beto avoid such states as undesirable and/or other states may beidentified as desirable (e.g., as described below in connection topossible PTSD patients).

After the desired sleep state is determined using the rules, the desiredsleep state may be compared to the current sleep state for the patient,and a determination can be made whether the current sleep state differsfrom the desired sleep state. If the current sleep state differs fromthe desired sleep state, a recommendation message may be generated andan intervention (e.g., therapeutic action) may be initiated in responsethereto. For example, sensory stimuli can be generated to guide thesleep pattern of the patient toward the desired sleep state. Similarly,in some embodiments, an undesired sleep state may be determined, thesensory stimuli may be generated to guide the patient out or away froman undesired state. The sensory stimuli can be any stimuli that can besensed by a sleeping patient. According to some embodiments, sensorystimuli may include light, sound, smell, vibration, heat or cold,moisture, electric shock, and/or other stimuli that can be sensed by apatient. As described below in connection to FIG. 7, the generatedsensory stimuli may be based on a recommendation and/or intervention.

According to an embodiment, adjustments can be made to the sensorystimuli to lead the sleeping patient toward another sleep stage. Thesechanges can include adjustments in the magnitude or quantity, tone,quality, pattern, frequency, application location, or any otheradjustment to sensory stimuli. Even minute changes to sensory stimulimay be sufficient to lead the sleeping patient toward another sleepstage. The type, duration, intensity, and timing of generated stimulidepend on the current and desired sleep state and on whether a directtransition is physiologically possible or whether the sleeper needs tobe led through some intermediate sleep state(s) prior to reaching thedesired state. For example, if the sleeper is awake while the desiredstate is NREM Stage 2 sleep, soothing sounds may be generated to inducea transition from wakefulness through NREM Stage 1 sleep to NREM Stage2. If for an example the sleeper is in NREM Stage 3 sleep while thedesired state is NREM Stage 2 sleep, a combination of subliminal soundsand stroboscopic light flashes may be optimal. Continued monitoring ofthe physiological attributes of the patient can be used to determinewhether the intended transition from one stage to sleep to another hastaken place.

Accordingly to an embodiment, alone or in combination, adjustments canbe made to the patient's medical care. These adjustments may includechanges to medication dosage, medication type, rate of administration ofdosage, and the like. The adjustments may be based on the ailmentsuffered by the patient, the environment of care (e.g., ambient light,sound, etc.), abnormal signal patterns identified by the system and/orexperts, acute status of the patient, and the like.

According to some embodiments, if the current sleep state does notdiffer from the desired sleep state, then no stimuli (or therapeuticaction/adjustment) may be generated to guide the sleep pattern of thepatient, because the patient is already in an optimal sleep stage.According to other embodiments, if the current sleep state of thepatient matches the desired sleep state, one or more stimuli may begenerated to help maintain the current sleep state of the patient. Forexample, in a loud environment, a white noise may be maintained to keepthe patient in a desired sleep state. Similarly, a light may bemaintained in environments that have variance in ambient lighting.

Disturbances that may interrupt or negatively impact the sleep state ofthe patient may be identified, and a determination can be made as towhether any disruptive disturbances are present. Disturbances mayinclude loud noise, strong light, temperature of the sleepingenvironment, and/or any other potential distracters which may cause thepatient to wake up frequently or prematurely or prevent the patient fromspontaneously entering into deeper stages of sleep (e.g., as is likelyin a hospital or ICU environment). If disruptive disturbances arepresent and identified, the patient may be protected from thedisturbances by taking or initiating remedial actions. For example, iftoo much ambient light is present in the environment, the sleep guidancesystem may be configured to control the lights in the sleepingenvironment so to be dimmed or the blinds closed to block sunlight orother light from outdoors from entering the room, or an eye mask or setof tinted glasses may be provided to block ambient light from reachingthe patient's eyes. If the temperature of the room is too hot or toocold, a heating and ventilation system for the sleeping environment canbe adjusted to adjust the temperature of the room to a more optimalsleeping temperature. If too much noise is present, a set of noisecanceling headphones or earplugs may be provided, or white noise may begenerated to block out the noise. If no disturbances are identified orthe patient has been protected from the disturbances, the method returnsto the monitoring step.

FIG. 7 illustrates a flowchart for an example process 700 for diagnosisand therapeutic treatment of patients in accordance with the embodimentsdescribed herein. Process 700 may be implemented by the integratedsystem of FIG. 5 described herein. The process 700 may be implemented todetect abnormal signal patterns or conditions in the patient'sphysiological signals as described herein and utilize a sleep guidancesystem to implement intervention recommendations (e.g., generate one ormore stimuli to guide the patient's sleep) without the need forintervention by a user (e.g., medical personnel).

At step 710, an abnormal signal pattern or condition may be detected.For example, the DAU 110 may acquire physiological signals from apatient coupled thereto. The DAU 110 may then characterize the signalsto detect a condition indicative of needing an intervention and/ortransmit the acquired signals to computer system 390 or other computersystems for processing and detection. Such conditions may include, butare not limited to, the example conditions described below and inconnection to FIGS. 8-28 (e.g., through a comparison of power spectrawaveforms representative of the physiological signals). As describedabove, thresholds may be applied to the condition detection rules toavoid and/or minimize false detections. At step 720, the detectedcondition may be delivered to one or more components of the integratedsystem of FIG. 5 as a message, as described above. In some embodiments,the physiological signals may be transmitted as part of the notificationmessage. The message may be delivered to the cloud server 540 or otherdevices of system for storage and additional processing. In someembodiments, the signals may be delivered or transmitted to the sleepguidance system 530 and processed thereon. The condition may berecognized by processing of the signals as described herein.

At step 730, recommendation information may be retrieved. As describedabove, the recommendation information may be based on at least one of adetected condition, the patient's medical history or current healthconcern, environmental surroundings (e.g., hospital environment, ICU,etc.), current and/or historical sleep stages, and/or other rules toimprove sleep management. The recommendation information may bepre-determined and stored in the memory 324 of the DAU 110. In otherembodiments, the recommendation information may be stored in the cloudserver 540, the computer system 390, the computer system 560, themobile-device 550, and/or a data storage accessible to the sleepguidance system 530. The recommendation information comprise one or moreinterventions for adjusting the patients case, for example, one or morestimuli and/or therapeutic action as described above.

At step 740, one or more intervention for application to the patient maybe determined based on the recommendation information. In variousembodiments, the intervention may be a stimuli or other therapeuticaction determined by the sleep guidance system 530 for managing sleepquality and circadian rhythms as described herein. In some embodiments,the intervention may be determined by and controlled by the integratedsystem of FIG. 5. The intervention may include one or more of, but notto be limited to, tactile vibrations (e.g., vibrotactile), audible(e.g., sounds, music, etc.), visual (modifying ambient light such asadding blue light), changes in temperature (e.g., adding heat orapplying a cooling sensation) to the patient, adjustments to medicationadministrated (e.g., increase/decrease dosage, administer differentmedications, increase/decrease rate of medication), adjustments topatient monitor peripherals, and the like.

At step 750, the sleep guidance system 530 may generate and/or othercontrol medical equipment based on the determined one or moreinterventions to manage the sleep quality of the patient. For example,if non-convulsive seizure activity is detected in the signal patterns, astandard response may be to reduce the level of sedation, prescribe ananti-convulsive medication, and begin monitoring the EEG with a full10-20 montage.

In other embodiments, an intervention may include generation ormodification of stimuli to lead the patient toward a desired sleep stateor away from an undesired sleep stage. For example, if irregularsleeping patterns are detected (e.g., insomnia and/or circadian rhythmdisorder) in the signal patterns, one or more of the stimuli may beadjusted to lead the patient away from an awake state or toward adesired sleep stage. For example, if the patient is awake while thedesired state is NREM sleep, soothing sounds, changes in heat or coldapplied to the facial area, or light can be used to induce a transitionfrom wakefulness to NREM. Continued monitoring of the physiologicalstate of the patient can be used to determine whether the intendedtransition from one stage to sleep to another has taken place so totreat insomnia and other sleeping disorders.

In another example, the systems and methods described herein may beconfigured to stage sleep in real-time in combination with delivery ofone or more stimuli to suppress an undesired sleep state. In someembodiments, such staging may be used to trigger an interventionrecommendation for a disease state or abnormal condition. In someembodiments, detection of REM sleep and/or transitioning toward REMsleep may trigger the intervention notification.

For example, if the patient is in currently in REM, at step 740 and 750,one or more stimuli may be determined and generated so as to lead thepatient out of REM and into NREM. If the patient is in NREM, at step 740and 750, the one or more stimuli may be generated so to maintain NREM.For example, the one or more stimuli may include delivery ofvibrotactile and/or blue light to suppress REM sleep.

In one embodiment, the applied intervention may be adaptive to theindividual to deliver the least amount of intervention needed to shiftthe user from REM to non-REM sleep. For example, if the user does notrespond to a stimuli based intervention within a minimum time duration(e.g., 1 min) the delivered stimuli may be adapted (i.e., frequency,intensity and/or duration increases). In various embodiments a REMavoidance intervention may enables the user to shift sleep stageswithout entering an awakened state. In other embodiments theintervention may cause the user to awaken as a means to avoid REM sleep.One skilled in the art will recognize that numerous approaches could beapplied to shift a patient from REM to non-REM sleep while avoiding anawakening state including by modifying or adjusting a frequency,intensity and/or duration of the applied one or more stimuli. While theforegoing examples are described in connection with the sleep guidancesystem 530 and the integrated system of FIG. 5, it will be appreciatedthat the describe interventions and/or rules for leading the patient'ssleep stage can be performed by an identified specialist and/orcaregiver as described above in connection to FIG. 5. Furthermore, insome embodiments, the specialist and/or caregiver may optionallyintervene with the automated process of FIG. 7, for example, byreceiving the recommendation information and instructing the systems toapply the intervention remotely and/or modifying the intervention inaccordance with improving patient care for special cases that may not becovered in standard rules.

FIG. 8 is a flow diagram of a process 800 for monitoring physiologicalsignals according to embodiments herein. The process 800 may be used tomonitor physiological signals from a patient to asses sleep quality of apatient. In some embodiments, process 800 may be implemented to detectand/or otherwise characterize the etiology of sleep quality of apatient. The process 200 may be used in coordination with providingmedical services (e.g., treatment, medication, etc.) to increaserecovery time of a patient and/or improve outcomes of medical treatment(e.g., increase in probability of successful treatment and/or improvedresults of treatment). For example, without subscribing to a particularscientific theory, it is believed that improved sleep quality andmanagement may be advantageous in recover over aliments and injuries. Apatient may be more likely to recover quicker if they are able toachieve necessary sleep quality (e.g., proper circadian rhythm and/orconsolidating sleep to nocturnal hours). For example, process 200 mayallow staff with limited neurophysiological training or expertise todetect abnormal patterns which slow patient recovery and/or increasemortality. FIG. 8 may be implemented using the various systems describedabove in FIGS. 1-5.

The process 800 begins at step 805 with the acquisition of physiologicalsignals from a patient by a DAU (e.g., DAU 110). As described above, theDAU performs concurrent measurements of two categories of signal data:(1) signal data related to sleep states, and (2) signal data related tothe type of sleep disruption. In an embodiment, the apparatus used toacquire the physiological signals ideally uses electrodes and sensors,such as sensor strap 120, that can be self-applied with limited skin orscalp preparation, and which monitors signal quality during use andprovides user feedback when signal quality problems are detected. Thecollected physiological signals may include rhythmic activity as well astransients. In some embodiments, the physiological signals include anyone or more of alpha signals, sigma signals, beta signals, deltasignals, theta signals, EMG signals, EEG signals, and EOG signals.Additionally, the physiological signals made include or be provided withacoustic signal data and movement signal data. Each physiological signalmay be representative of a frequency band, as described above, andrepresented by a power spectra waveform as described above andillustrated herein.

Once the physiological signals are obtained, these signals arecharacterized (step 810) for additional process steps to carried out invarious implementations. In some embodiments, characterizing thephysiological signals may comprise or otherwise be part ofcharacterizing an etiology of sleep of the patient associated with theDAU 110. Characterization herein may include, for example, a comparisonamongst the physiological signals acquired. For example, the powerspectra waveform of a first physiological signal may be compared withone or more other power spectra waveforms of the physiological signalsto characterize and identify disruptions to sleep quality. As describedherein, these comparisons may be used by the systems (e.g., asillustrated in FIG. 5) and/or caregivers to manage a patient's sleep.Comparison of the power spectra waveforms of the various physiologicalsignal data may be beneficial in detecting conditions that are externalto the patient (e.g., due to the environment in which the patient islocated, treatment plans, medicine administered, etc.) as well as foridentification of sleep arousals and/or disruptions due to sleepdisorders. Conventional systems that do not utilize the power spectrawaveforms as described herein may not be capable of identifying suchconditions.

For example, in one embodiment, the process 800 stages the patient'ssleep at step 815 (e.g., FIG. 12); identifies awakenings, arousals andperiodic patterns at step 820 (e.g., FIG. 13); and then monitors stagingpatterns and intervenes to limit daytime sleep and consolidate nocturnalsleep at step 825 (e.g., FIGS. 5, 7, 29 and 30). In another embodiment,alone or in combination, the process 800 identifies abnormal slow waveactivity (ASWA) during sleep and/or awake at step 830 (e.g., FIG. 15)and evaluates patient (e.g., initiate or continues treatment) for sepsisat step 835 (e.g., FIGS. 6 and 7). In another embodiment, alone or incombination, the process 800 identifies burst-suppression at step 840(e.g., FIGS. 21A-23B) and reviews currently administered medication thatcan exacerbate the condition and modify as required at step 845 (e.g.,interventions of FIGS. 6 and 7). In another embodiment, alone or incombination, the process 800 identifies non-convulsive epileptiformactivity at step 850 (e.g., FIGS. 24A-24C); evaluates currentmedications that can exacerbate the condition at step 855; initiates afull montage, continuous EEG monitor at step 860 (e.g., as anintervention of FIGS. 6 and 7); and administers anti-seizure medicationat step 865 (e.g., as an intervention described in FIGS. 6 and 7). Asdescribed above, according to some embodiments, the acquiredphysiological signal data can be downloaded to an external computersystem 390 for processing or, in some embodiments, by firmware includedon DAU 110. Alternatively, or in combination, the data may be displayedby the external computer system 390 for visual inspection andidentification by users.

In an embodiment, various automated algorithms can be applied to capturesignal data. For example, the EEG signals may be subjected to a filterbank that decomposes the signals into the frequency bands commonly usedin the EEG analyses: eye movements/artifacts (<1 Hz), delta (1-3 Hz),theta (4-7 Hz), alpha (8-12 Hz), sigma (12-16 Hz), beta (18-30 Hz),EMG/artifacts (>32 Hz). These power bands can be used to characterizesleep architecture and sleep continuity, as well as for visual and/orautomated inspection of the relevant patterns. The frequency cutoffs forthese power bands can be modified as needed to characterize sleep andwake. For example, further sub-characterization of the frequencybands/bins, and or sub-analysis of the signals above 40 Hz can beemployed for this purpose. Those skilled in the art will recognize thatany other frequency band can also be used where advantageous. Thoseskilled in the art will also recognize that the filter bank can berealized with FIR filters, IIR filters, wavelets, or any other similartechnique for time-frequency decomposition of signals.

In one embodiment, once the physiological signals are acquired from thesensors, the signals may be analyzed to characterize the signals (step810) and stage the sleep of the patient (step 820). For example, averagepower spectra analysis computed across stage N1, N2, N3 (SWS) and REMstates in the delta, theta and alpha ranges can be used to identifyabnormal characteristics associated with abnormal sleep characteristics.In at least one embodiment, the power spectra may be extracted from thefrequency bands described above. In some embodiments, the extractedsignal power spectra may be averaged into periodic epochs (e.g., (e.g.,30 seconds) for staging sleep. While 30 second epochs are describedherein, these are merely illustrative and other periodic epochs may beutilized as necessary for a desired sensitivity and analysis range. Boththe absolute and relative power between and cross bands may be used toextract useful information that accommodates between differences in therelative power of the signals detected from the patient. For featureextraction, power spectra values can be extracted at any resolution, forexample a resolution greater than 16 Hz. In at least one embodiment, thepower spectra are presented and/or processed to enable recognition ofpattern changes associated with sleep and wake or abnormal neurologicalpatterns. While the power spectra values may be collected at anyfrequency. In one embodiment, the spectra power values may presentedand/or analyzed at a frequency based on matching the frequency to thepixel resolution of display (e.g., 1 hz).

For example, FIGS. 9A-9C are an example of data illustrating acquiredand characterized single patterns according to embodiments herein. FIG.9A illustrates a 30 minute data acquisition of the plurality offrequency bands, while FIGS. 9B and 9C illustrate example 30 secondepochs characterized as stage N2 and awake, respectively. Additionally,FIGS. 9A-9C illustrate a subtle decrease in frontal tone that may occurduring sleep only, which can be difficult to detect visually in the EEGwaveform. However, when the EMG power is lower than alpha, sigma, andbeta waveforms, as shown in FIGS. 9A and 9B, the user is typicallyasleep. In some embodiments, the prominence and similarity of the EEGdelta and theta waves in FIGS. 9Ba and 9C, even though the EMG powerincreased by, for example, six-fold, may suggest a high likelihood ofvisual misinterpretation of a sleep stage (e.g., as described below inconnection to FIG. 12) of the patient without access to the relativepower of all of the bands. In standard sleep stage, the number ofawakenings (i.e., transitions between sleep, wake, and a return tosleep) may range from 2 to 5 occurrences per hour.

In some embodiments, identifying the patient sleep stage may be based acomparison of relative power spectra of one or more frequency bands. Forexample, where the EMG power is low, the magnitude of the beta power mayindicate the user is in either Stage N1 or rapid eye movement (REM)sleep, for example, as shown in FIGS. 10A-10C. As another example,during REM (in patients not on medication), the sigma power may betypically lower relative to beta and alpha, as shown in FIG. 10C. Duringstage N1, the sigma power may typically be the approximately the same inmagnitude as the beta power, as shown in FIG. 10B. In some embodiments,to reduce the likelihood of a sleep stage misclassification resultingfrom medications (i.e., Stage N1 misclassified as REM), low rollingocular activity as shown in FIG. 10C may be distinguished from a moresharp edged phasic ocular activity which occurs only during REM. Thus,various embodiments may characterize one or more of the plurality ofphysiological signals to minimize misclassification of sleep stages.Accordingly, FIGS. 10A-10C may include example signal patters of lowvoltage EEG compared with elevated beta power that may requiremodification to staging rules to avoid and/or minimize misclassificationof staging rules as a result of medication(s) (e.g., FIG. 12 below).

FIGS. 11A-11C provide example data of EEG waveforms that may impactdelta power relative to one or more of the other frequency bands.Particularly, FIGS. 11A-11C may be at least on example of ocular and EEGwaveforms having an impact on delta power relative to other powerfrequencies. For example, delta power is illustrated relative to thetheta, alpha, sigma, beta and EMG bands in a healthy adult during slowrolling ocular activity (stage N1, e.g., FIG. 11A), rapid eye movementsleep (stage REM, e.g., FIG. 11B), and slow wave sleep (stage N3, e.g.,FIG. 11C). Delta power may be influenced by the negatively correlatedocular activity at sleep onset (stage N1) and during REM. Delta wavesduring slow wave sleep may be more asynchronous. In at least oneembodiment, filters, as described above, may be applied to extract thedelta power before and after removal of ocular activity. The detectionof ocular activity can be achieved by comparing the phase of the signalscontaining left and right ocular activity. For example, the signals maybe negatively correlated during both slow roller ocular activity (e.g.,associated with sleep onset) and phasic ocular activity (e.g., REM).However, the magnitude and variability of the negative association maybe much greater during REM, as shown in FIG. 11B. FIG. 11C illustratesthat a slow roller or slow wave ocular activity and most other artifactscan be removed from the delta power via filtering, for example, a sharpfilter at 1 Hz or less. In another embodiment, alone or in combination,changes in the median power of the delta activity can be used to detectsharp edges in the signal associated with ocular activity but nothealthy slow wave sleep. Once this sharp edged ocular activity isdetected, it can be used to decontaminate the delta power. True slowwave brain activity can be detected when the magnitude of delta power issufficiently large and the differences in delta power before and afterdecontamination of ocular activity are minimal.

In some embodiments, the delta power may be interpreted relative toother EEG power characteristics. For example, since the magnitude ofdelta power can be influenced by both ocular (e.g., FIGS. 11A and 11B)and EEG activity (e.g., FIG. 11C), delta power may also need beinterpreted relative to the other EEG power characteristics. During bothstage N1 and REM the delta power can be interpreted relative todecreased theta and sigma power, and increased beta and EMG power (e.g.,FIGS. 11A and 11B). During slow wave sleep (SWS) (e.g., FIG. 11C), sigmapower may be more prominent as compared to beta and EMG power.

In certain implementations, when a hospitalized patient is administereda medication (e.g., sedative, analgesic, etc.) to manage agitation,pain, or other ICU condition or induce sleep, EEG power spectralcharacteristics used to stage sleep (e.g., step 820) may be influencedby the type and amount of medication. Medications typically administeredin the ICU distort the relative power in the alpha, sigma, beta, and EMGbands, and suppress sleep spindle and slow wave activity which occurs innormal, healthy sleep. Epochs that are staged N2 with a combination ofrelatively low sigma power and/or increase alpha power can be indicativeof a medication/sedation effect (e.g., the left half of FIG. 9A and/orFIG. 9B). Sedative-induced sleep may increase beta activity which canresult in the misclassification of REM sleep. Additionally, criticallyill patients can have abnormal EEG patterns which can contribute toincorrect sleep staging. Thus, embodiments herein may be configured tomodify sleep staging rules based on abnormal signal patters due, inpart, to administered medication.

For example, FIG. 12 provides a flowchart of an example process 1200 formodifying standard sleep staging rules according to embodiments herein.For example, FIG. 12 may be implemented following step 810 of FIG. 8(e.g., as part of step 820) or as a separate process. FIG. 13 may beimplemented using the various systems described above in FIGS. 1-5. Insome embodiments, the modification of sleep staging rules may be based,in part, on a hospitalized condition of a patient and/or medicatedcondition.

The process 1200 begins at step 1210 begins with automated analysis todefine sleep by standard staging rules. For example, in someembodiments, once the physiological signals are obtained from thesensors, the signals may be analyzed to assess the sleep stage of theuser (step 1210). As described above, according to some embodiments, theacquired physiological signal data can be downloaded to an externalcomputer system 390 for processing or, in some embodiments, by firmwareincluded on DAU 110. Alternatively, or in combination, the data may bedisplayed by the external computer system 390 for visual inspection andidentification by users. According to an embodiment, the physiologicalsignals acquired by the DAU 110 can be downloaded to external computersystem 390 and stored in a memory.

In an embodiment, various automated algorithms can be applied to thecaptured signal data. For example, the EEG signals are subjected to afilter bank that decomposes the signals into the frequency bandscommonly used in the EEG analyses: eye movements/artifacts (<1 Hz),delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), sigma (12-16 Hz), beta(18-30 Hz), EMG/artifacts (>32 Hz). Those skilled in the art willrecognize that any other frequency band can also be used whereadvantageous. Those skilled in the art will also recognize that thefilter bank can be realized with FIR filters, IIR filters, wavelets, orany other similar technique for time-frequency decomposition of signals.

In some embodiments, REM sleep can be distinguished from non-REM sleepon the basis of ratios between beta EEG power (e.g., 18 to 32 Hz) anddelta power (e.g., 1 to 3 Hz) within a pre-defined time window, or onthe basis of a measure of agreement between the 2 EEG signals acquiredsimultaneously. The measures of agreement, when calculated over a shorttime window (e.g. 2-5 seconds) will behave markedly differently in caseof eye movements than in case of delta waves (which can easily beconfused with each other if only frequency analyses are used). Accordingother embodiments, any statistical measure of agreement, such asPearson's correlation coefficient or coherence, can be used for thispurpose. Ratios of delta (e.g., 1 to 3.5 Hz) to beta (18-32 Hz) andtheta (4-7 Hz) power are used to identify slow wave sleep.

In alternative embodiments, alone or in combination, the detection ofsleep stages can be performed using more sophisticated linear ornon-linear mathematical models (e.g., discriminant function, neuralnetwork, etc.) with variables that can be obtained from the EEG, EOG andECG signals. Short duration fast-frequency EEG bursts are measured usingone-second measures of power spectra to detect sleep spindles (that onlyappear during Stage 2 sleep) and EEG arousals (that appear in Stage 1sleep). The distinction between the spindles and arousals can be made onthe basis of their duration (spindles are shorter, arousals longer than3 seconds). One skilled in the art will recognize that in addition tothe techniques mentioned above, ratios of the power in various frequencybands, or linear combinations (weighted sums) of the power in variousfrequency bands can be used for separation of sleep states andwaveforms. In addition to power spectra analysis of the EEG, one skilledin the art will recognize that variability in the ECG signal increasesduring rapid eye movement sleep. These patterns are different from therapid bradycardia-tachycardia changes that occur as a result of anarousal or with the sinus arrhythmia that can be seen in children. In anembodiment, full-disclosure recording are optionally presented to allowthe signals and automated sleep staging to be manually viewed and editedusing a user interface provided by the data processing and visualizationmodule of the external computer system 390. Standard sleep architectureparameters are then computed, including total sleep, REM and SWS times,sleep, REM and SWS latency, and sleep efficiency. Mean power spectraanalysis computed across stage N1, N2, N3 (SWS) and REM states in thedelta, theta and alpha ranges can be used to identify abnormalcharacteristics associated with abnormal sleep characteristics.

At step 1220, process 1200 stages Light N2 sleep to identify periods ofsleep that are influenced by medications. Once these periods of sleepare identified, the process 1200 applies thresholds, describe below, tolimit misclassification of REM (step 1230) and misclassification ofawake (step 1240) due to medications. The process 1200 may then identifystages misclassified as awake and/or N3 and reclassify these stages asneeded at step 1240, when abnormal slow wave activity (ASWA) is detectedin the characterized physiological signals.

For example, process 1200 may be used to identify periods of sleeps thatmay be influenced by medication or external conditions (e.g.,hospitalization). For example, a caregiver may determine when themedications are influencing the sleep/wake condition of a patient basedon the reported sleep stage under conventional staging rules. Suchmedications can cause steady, elevated alpha power, that result inoccasional cortical arousal(s) which may trigger an interruption instage N2 toward stage N1 or awake. These cortical arousal(s) may not beeasily detected. A medication effect that contributes to a steadyelevation of either alpha or sigma activity can also reduce thecapability to automatically detect alpha/sigma bursts associated withsleep spindle activity, which is a characteristic of healthy sleep. As aresult, a trigger used to transition from stage N1 to N2 may bedifficult to detect. Thus, classification of stages N1 and N2 may becomemore dependent on relative theta power, rather than use of arousals andsleep spindle events to trigger stage changes. As a result, long periods(e.g., >1 hour) of steady stage N2 in the absence of occasionalawakenings, for example, may be an indication that medication levels canbe reduced. In some embodiments, alone or in combination, medicationeffects can be further characterized by the classification of lighterstage N2 (e.g., Light N2) defined by elevated alpha or EMG activity inthe absence of sleep spindle activity. Long periods of light N2 mayindicate use of a medication that is disrupting healthy sleep. Anabnormal neurological EEG pattern, called burst suppression (describedbelow in connection to FIGS. 21A-23B), can be recognized by inspectionof the EEG associated with long uninterrupted periods of stage N2 orLight N2. Such inspection may be done visually by a user and/orautomated using one or more of the systems described herein.

In various implementations, identifying abnormal signal patterns basedon sleep stage may be based, in part, on the influence of medications toavoid misclassification. Thus, algorithms used to stage sleep may bemodified accordingly. As described above, FIGS. 10A-10C show an examplelow voltage EEG. The examples shown in FIG. 10A-10C also depictmedication induced elevated beta power relative to the alpha and thetapower, coupled with increased delta power resulting from slowfluctuations (e.g., less than 0.5 Hz), which may be used to avoid and/orminimize misclassification of stage REM. In other instances, medicationcould elevate the alpha power relative to theta and sigma power, alsoresulting in a staging misclassification. Such situations may also beidentified in step 1220.

Medications can also increase the magnitude of power in the EMG band,providing another example of step 1220. This may result in epochs thatmay be visually staged as sleep, being classified by an automated meansas awake. FIGS. 9A-9C provide such an example. FIG. 9A depicts signalpatterns collected on a 30 minute time scale and the EMG power increasesinexplicably and remains high and steady throughout the remainder of thedata collection. Whereas, on a 30 second time scale, the EEG signalslook markedly similar despite the magnitude difference in EMG power usedto assign the epoch as stage N2 vs. awake.

In one embodiment, the systems herein may identify a stage of awakeand/or stage N3 when the patient is actually in another stage. Thus,based on detecting abnormal slow waves, the process 1200 may reclassifythe stages as N2. Additionally, in the various embodiments, thresholdsmay be employed to account for influence due to medications. Forexample, thresholds used to stage sleep may be adjusted to accommodatethe influence of medications to maintain the accuracy of the sleep/wakestaging. In some embodiments, thresholds used ensure medications do notcontribute to the misclassification of REM (e.g., step 1230) include,but are not limited to, the ratio between alpha/beta power, alpha/sigmapower, theta/beta power, theta/EMG power, delta power, and/or thecorrelation between the left and right eye movements.

In at least one embodiment, when excessive EMG power is resulting in theEEG being staged as awake when the patient is asleep, the algorithmthresholds may be automatically adjusted or permit the caregiver toadjust the threshold to accommodate this condition. For example, if thecaregiver visually detects elevated EMG and the patient is asleep, thesystems described herein may enable the caregiver to increase thethreshold so that the system stages the periods as light sleep. In someembodiments, the increase may be implemented by the system (e.g.,computer system 390), for example based in part on a determination thatthe person is asleep (e.g., through an absence of body movement or thelike).

FIG. 13 illustrates a flowchart of an example process 1300 formonitoring physiological signal patterns to identify patterns that maydisrupt sleep and compromise sleep quality. FIG. 13 may be implementedas part of step 820 of FIG. 8 or as a separate process. FIG. 13 may beimplemented using the various systems described above in FIGS. 1-5.

In some embodiments, monitoring physiological signal patterns may bebased, in part, modified staging rules as described above in connectionto FIG. 12. For example, the staging rules may be modified based onmedication provided to the patient, hospitalization of the patient,and/or the patient being admitted to the ICU. Process 1300 begins withacquiring (step 1305) and characterizing (step 1310) physiologicalsignals as described above (e.g., steps 805 and 810 of FIG. 8). Theprocess 1300 then proceeds with staging sleep using modified stagingrules at step 1315 (e.g., as modified according to FIG. 12). The process1300 then identifies abnormal EEG power spectra patterns (step 1320),periodic awakenings (step 1325), periodic cortical arousals (step 1330),periodic movement patterns (step 1335), periodic sound patterns (step1340), and periodic hear rate patterns (step 1345). In some embodiments,one or more of the identified patterns of steps 1320-1345 may be used,for example, as part of step 820 of FIG. 8 and/or to reclassify thestages in steps 1250 of FIG. 12.

In some embodiments, the systems herein may be configured toautomatically identify conditions via the process 1300 that areaffecting the patient's quality of sleep and utilize this information.One or more of the plurality of physiological channels may be utilizedto identify and compare patterns to infer the presence of a conditionand/or effect of medication. The systems may then utilize thisinformation in, for example, modifying sleep staging rules (e.g., FIG.12) or identifying interventions for improved care (e.g., FIGS. 6 and7).

For example, an implementation of step 1340 may include a sound channelthat can be monitored to recognize sound patterns (e.g., step 1340).Certain sounds may interfere with a patient's ability to fall asleep orcause the patient to arousal from sleep. Thus, the DAU 110 may utilizethe acoustic microphone 314 to detect sounds above a predetermined limitthat may cause such interference. In some embodiments, a sound over 40dB may interfere with their ability to fall asleep or cause the patientto arousal from sleep. However, any limit may be applied based on theparticular environment. For example, the limit in a hospital may begreater than the limit for a quiet room. In some embodiments, when thepatient is mechanically ventilated, repetitive patterns of periodicawakening can suggest dyssynchronous breathing or an incorrect settingof respiratory frequency. In some embodiments, the periodicity may beset to <2 min intervals, however other limits may be applicable. If thepatient is extubated, repetitive disruptions can be attributed tountreated obstructive sleep apnea (OSA) and/or sleep disorderedbreathing (SDB).

As many as 40% of patients over the age of 50 who undergo generalanesthesia have undiagnosed SDB. SDB may only be observable when apatient attempts to spontaneously breath (i.e., is not mechanicallyventilated). Thus, SDB is typically confirmed using nasal airflow andoximetry signals. However, by using the systems and methods describedherein, SDB patterns may be distinguishable using other physiologicalsignals (e.g., FIG. 13).

For example, FIGS. 14A and 14B illustrate data of an example of signalpatterns indicative of sleep disordered breathing. The sleep disorderedbreathing may be detected (e.g., visually or automatically using acomputer) by the characterization of the physiological signals acquiredwith the DAU 110. For example, a crescendo pattern in snoring may beindicative of a collapsing airway (e.g., FIG. 14A). In someimplementations, this crescendo pattern may be identified through step1340. The abrupt termination of crescendo snoring may be indicative ofthe return of airway patency and resumption of breathing (e.g., FIG.14B). The arousal (e.g., identified in step 1330) associated with theresumption in breathing may be identified in multiple physiologicalsignals. For example, arousals may be identified in brief increases anddecreases in autonomic activation appearing in the heart/pulse ratesignal (e.g., step 1345), head movements (e.g., step 1335) associatedwith a gasp, and increases in alpha and EMG activity (e.g., step 1320)relative to the other power spectra corresponding to cortical or microarousals (e.g., FIG. 14B). One or more or all of the SDB confirmatorypatterns may be apparent with each SDB event (e.g., FIG. 14B). SDB mayoccur in repetitive patters of known durations (e.g., 30 sec to 120 secdurations), detection may be enhanced when the signals are viewed on atime scale greater than the shortest duration, for example, 30 sections(e.g., the standard time scale for staging sleep). Sleeping position mayadditionally be used to confirm SDB patterns given SDB and typicallymore severe when the patient is sleeping supine (e.g., the patient is onhis/her back).

While an implementation of FIG. 13 is described above with reference toSDB, it will be appreciated that other conditions may be identified inaccordance with the disclosure herein. For example, other ailmentsand/or medication effects on sleep have been and will be described inconnection with FIGS. 8-28. Thus, one skilled in the art will understandhow to implement each type of data to define rules for identifyingconditions in accordance with FIG. 13.

In an embodiment, it may be advantageous to monitor for and/or identifyabnormal EEG waveforms. These abnormal EEG waves may be polymorphicdelta activity, triphasic waves, and/or sepsis-associatedencephalopathy, which may be collectively referred to as abnormal slowwave activity (ASWA). As described above, according to some embodiments,the acquired physiological signal data can be downloaded to an externalcomputer system 390 for processing and identification of the ASWA or, insome embodiments, by firmware included on DAU 110. Alternatively, or incombination, the data may be displayed by the external computer system390 for visual inspection and identification by users.

The representation of ASWA (either visually or in processing) is similarto slow wave sleep (i.e., large delta waves). However, ASWA can occurduring sleep or wake with eyes open or closed. FIG. 15 illustrates aflow chart of an example process 1500 for the automated detection ofASWA. FIG. 15 may be implemented as part of step 830 of FIG. 8 or as aseparate process. FIG. 15 may be implemented using the various systemsdescribed above in FIGS. 1-5.

In some embodiments, detection of ASWA may be based in part onidentification of sleep stages of a patient. The sleep stages, in someembodiments, may be in accordance with convention sleep staging rulesand/or otherwise modified as described in connection to FIG. 12 above.Process 1500 begins with acquiring (step 1505) and characterizing (step1510) physiological signals as described above (e.g., steps 805 and 810of FIG. 8). The process 1500 then proceeds with identifying a sleepstage at step 1515, identifies a plurality of abnormal characteristicsof slow waves to detect and/or identify ASWA at steps 1520-1550, andthen optionally presents the results at step 1555. In some embodiments,presenting results 1555 may comprise at least one of automaticallyidentifying an intervention based, in part, on the ASWA as describedherein (e.g., FIG. 5 and/or FIG. 7) and/or visually representing theresults to a user to facilitate user action based thereon.

The flow chart in FIG. 15 provides one approach for the automateddetection of ASWA. An elevated delta power (e.g., 1-4 Hz) can beattributed to numerous factors, including brain activity (slow wavesleep), ocular activity (e.g., slow roller and rapid eye movements, andblinks and saccades), and artifacts (e.g., respiratory, sweat, andmovement). Thus, further characterization of the signal is needed todifferentiate ASWA from other factors that increase delta power (i.e.,ocular activity and artifact). Accordingly, in the example of FIG. 15,identifying ASWA may include at least one or more of: identifying largeamplitude slow waves (step 1520), identifying abnormal sharp edges inslow waves (step 1525), identifying abnormal theta power across slowwaves (step 1530), identifying abnormal alpha power across slow waves(step 1535), identifying abnormal sigma power across slow waves (step1540), identifying abnormal beta power across slow waves (step 1545),and identifying abnormal EMG power across slow waves (step 1550).

In various embodiment, epochs can be first staged using algorithmsdesigned to mimic the standard sleep staging rules (as described abovein connection to step 1210 of FIG. 12), and only those periods detectedas awake and stage N3 may be evaluated for ASWA (e.g., FIG. 15). Thephysiological signals are evaluated for ASWA without consideration ofthe sleep stage (e.g., between awake or N3 sleep), because ASWA mayoccur during both sleep and wake (e.g., with or without frontal muscletone). In various embodiments, the method previously described to detectthe sharp edges in ocular activity (e.g., FIGS. 10A-10C) can also beused to differentiate normal slow wave brain wave and ASWA. In anotherembodiment, sharp edged waveforms may be viewed on a predetermined timescale (e.g., 30 seconds) and the magnitude of the EMG may be viewed on asecond predetermined time scale (e.g., 30 minutes) as a way to identifyand detect ASWA. The time scales may be any desired time scale necessaryto fully evaluate and identify ASWA, and need not be the same ordifferent.

FIG. 16 includes data illustrating example physiological signal patternsthat may be used for detecting ASWA in accordance with FIG. 15. Forexample, FIG. 16 depicts a data signal 1600 measured on a long timescale (e.g., 30 minutes) including three ASWA periods with markedlysimilar EEG waveforms (e.g., 1610, 1620, and 1630, each shown on ashorter time scale of 30 seconds). FIG. 16 illustrates that it may bedifficult to detect (e.g., visually) an increased frontal muscle tonethat occurs when awake stage is difficult to detect, as is an increasedEMG power relative to the other power bands. Without detection of therelative increase in EMG power (1600) on a longer time scale, it wouldbe difficult to distinguish between an awake and sleep conditions on atraditional 30 second time scale. Although the characteristics of ASWAinclude suppressed theta and sigma relative to delta and alpha power,respectively, visual recognition of these patterns using only the EEGwaveform (e.g., comparing the EEG waveform in FIG. 11C with that of1610) is difficult, even with the presentation of the associated powerbands. Manual or automated characterization of ASWA (i.e., abnormalbrain activity) as stage N3 (i.e., healthy, deep sleep) my not onlyinaccurate, but also may compromise the possible early recognition ofthe onset of sepsis. Differentiating between ASWA from N1, N2, and N3can be achieved by the characterization and identification of suppressedtheta and sigma power relative to delta and alpha power, respectively(e.g., steps 1530-1550). The magnitude of EMG power may also be used todifferentiate abnormal ASWA during sleep and awake.

The identification of sharp edged brain wave patterns (e.g., step 1525)can also be used to differentiate ASWA associated with polymorphic deltaactivity or sepsis-associated encephalopathy (SAE) from ASWA associatedwith frontal intermittent rhythmic delta activity (FIRDA). For example,FIG. 17 includes example data illustrated physiological signal patternsindicative of ASWA associated with FIRDA. The data of FIG. 17 is anillustrative example of physiological signal patterns with in-phasedelta activity that may be an indication of FIRDA. FIRDA is anothermanifestation of abnormal brain activity that benefits from analysis ofa 30 sec time scale as shown in FIG. 17. Recognition of FIRDA maybenefit from access to both differential and referential EEG recordings,because the magnitude of the FIRDA activity is attenuated when amplifiercommon mode rejects differential signals with high coherence. FIG. 17illustrates an example showing both an attenuated differential signal aswell as referential signals presents in the LEOG and REOG channels.

In an embodiment, abnormal power in the theta, alpha, sigma, beta andEMG bands (e.g., as identified in steps 1530-1550) may be combined witha magnitude of decontaminated delta power and the sharp edged slow waveactivity to further characterize and identify ASWA (e.g., differences insignals staged N3 in FIGS. 11A and 11B).

In various embodiments, machine learning techniques may be utilized toemploy the remaining steps of FIG. 15 in order to differentiate ASWAfrom other conditions, e.g., ASWA vs. healthy slow wave activity,healthy awake, FIRDA, ocular activity, or artifact, etc. For example,the acquired physiological signal data can be downloaded to an externalcomputer system 390 comprising machine learning software executed by aprocessor for processing and identification of the ASWA. For example,the external computer system 390 may be configured to build a databaseof physiological signals that have been differentiated from otherconditions, which may be accessed as part of the identification of ASWAin subsequent implementations of FIG. 15. Alternative approaches tomachine learning can be used for the purpose of ASWA detection, e.g.,detection of values that exceed empirically defined thresholds. Bothabsolute and relative power values can be used for this step, and theratio among the bands may also be useful in the characterization ofASWA.

While the use of machine learning is described in connection withdetection of ASWA. It will be appreciated that machine learningtechniques can be utilized to detect any of the abnormal physiologicalsignals patterns described throughout this disclosure. For example,machine learning techniques may be implemented to recognize signalpatterns indicative of any of the conditions described in FIGS. 8-28,and thus determine that the recognized pattern is indicative of anassociated abnormal condition or pattern. That application to ASWA ismerely intended as an illustrative example. Example machine learningalgorithms include, but are not limited to, artificial intelligence,image processing techniques (e.g., machine vision, stitching, filtering,thresholding, pixel counting, segmentation, edge detection and tracking,color analysis, object recognition, pattern recognition, blob detectionand extraction, optical character recognition, and the like), parsing ofdata objects and/or associated metadata, and the like. Thus, thecomputer systems described herein may be configured to automaticallyrecognize an abnormal signal pattern, associate the recognized patternwith an abnormal condition thereby detecting the abnormal condition, andeither report the presence of the abnormal condition and/or take actionin response thereto as described above in connection with FIGS. 6 and 7.

In at least one embodiment, the alpha power can be normalized (e.g.,divided by) the sum of the theta, alpha, sigma, beta and EMG power bandsto accommodate individual differences in the generation of alpha powerand the impact of medications on the absolute alpha power. Additionalratios useful in the detection of abnormal slow waves include thealpha/beta, theta/EMG, alpha/EMG, theta/sigma, delta/beta and sigmanormalized to the sum of all six frequency bands. One skilled in the artwill recognize that different ratio combinations can be computed andemployed to improve the sensitivity and specificity of the signalpattern detector.

Additionally, the cut off frequency and sharpness of the low pass filterapplied to the EEG signal (e.g., at step 1510) may affect the magnitudeof the power measured in the EMG range. For example, the difference inEMG power, when different low pass filters are applied, may be used toassist in differentiating elevated EMG associated with increased muscletone when awake from elevated EMG power resulting from the influence ofsharp edged ASWA. In an example embodiment, ASWA may be detected forboth sleep and awake conditions, and annotated for visual detection in amanner similar to that illustrated in FIG. 18 with Ab3 (e.g., normalizedsum of power bands in stage N3) when asleep and AbWake (e.g., normalizedsum of power bands) when awake. FIG. 18 illustrates an example of ASWA,where EMG power may assist in the properly staging a current sleep stageas AbWake when awake and Ab3 when asleep.

Returning to FIG. 8, recovery during hospitalization may include, notonly adequate quantity and quality of sleep, but also effectivemanagement of abnormal neurological activity (e.g., steps 825-265).Other than patients in a neurological intensive care unit or havingwitnessed convulsive seizures, the EEG is not routinely monitored, eventhough burst suppression and non-convulsive epileptiform activity isrelatively common in hospitalized patients and is associated with lessfavorable outcomes (e.g., slow or non-recovery). One of the reasons thatEEG is not routinely monitored in hospitalized patients is that atrained EEG technician typically is needed to apply the full montage,continuous EEG acquisition system. Additionally, these conventional EEGacquisition systems are large and expensive, and thus further limitroutine monitoring on all patients as a precaution. Another limitationof conventional EEG is that an EEG technician and/or neurologist (e.g.,expert) is needed to monitor the signals in real time to detect abnormalpatterns.

Traditionally, conventional EEG systems were needed to detect the focalsite of a seizure. Furthermore, recognizing the occurrence ofnon-convulsive seizure activity can only be detected in the vastmajority of cases using a limited channel monitoring device such asthese conventional EEG systems, which were only used intermittently dueto complexity and costs. In contrast, the systems and methods disclosedherein (e.g., DAU 110 and the system of FIG. 5) provide a light,relatively inexpensive means for monitoring EEG in a form factor thatcan be affixed by any caregiver with very limited technical training. Asdescribed above, the DAU 110 is light and mobile, and with an externalbattery pack affixed, it can be used for continuous, wireless recordingand monitoring of different combinations of physiological signals asdescribed herein. The DAU 110 may provide voice messages to identifywhen signal quality is poor. Skin-sensor impedances can be acquiredperiodically to assist in the identification of poor signal quality. Invarious embodiments, the signals can be recorded to a memory in the DAU110 (e.g., a memory of the DAU 110 or a removable memory card) and maybe reviewed off-line by an expert. In some embodiments, alone or incombination, the EEG signals can be monitored remotely in real time byan expert. In at least one embodiment, the physiological signals aretransmitted to small, tablet size computer (e.g., mobile device 550 orcomputer system 560). Software installed therein (or in the computersystem 390) may be executed by a processor to characterize, analyze, andautomatically execute interventions such that a caregiver need not beconsulter or otherwise intervene with the sleep of the patient.Alternatively, or in combination, the software may be executed to alsopresent the characterized signals in a manner that provides a caregiverhaving limited technical training the capability to monitor thephysiological signal patterns. Based thereon, the caregiver may then beable to abnormal patterns similar to the monitoring of other vitalsigns, and to recognize when an expert is needed for a more carefulreview.

Mechanically ventilated patients are typically sedated and sedatedpatients generally have a sleep efficiency of at least 50% (i.e., asleepfor at least 50% of the attempted time). Elevated EMG burst activity maycause epochs to be improperly staged awake, and elevated EMG burstactivity can be attributed to ventilatory distress (e.g., incorrectpressure, problems with the breathing tube, asynchronous breathing,etc.). Thus, interpretation of EMG burst activity may require analysismultiple physiological signals, which may include any combination of thesound, power and LEOG, REOG and EEG signal panes, and time scales. Forexample, there may be a benefit to analyzing the signals on a time scalegreater than 30 seconds, for example, to confirm the EMG bursts are notcorrelated with changes in the sound channel (e.g., snoring sounds).

FIGS. 19A and 19B include data of an example physiological signalpattern of an elevated by steady sound experienced by the patient. FIG.19 is an illustrative example of signal patterns having gross excursionsof EMG power apparent only when the patient was in the supine positioncoupled with loud steady sound. The example data of FIG. 19 wascollected over a 2 hour time scale (e.g., FIG. 19A), with a relativelylarge, steady sound that is recorded (e.g., at step 1340 of FIG. 13)throughout the time scale (e.g., nocturnal hours in one embodiment).Sustained, gross excursions of EMG power may be observed when thepatient is in the supine position. FIG. 19B illustrates signal excerptson a 30 second time scale including bursts in the EMG powercorresponding to increases in sounds in, for example, a mechanicallyventilated patient. The steady, elevated sound coupled with the surge inEMG associated in the supine position that results in awakenings fromsleep may be an abnormal signal pattern (e.g., as identified by themethod of FIG. 15) that can disrupt sleep continuity. These small burstsderived from the EEG sensors appear to be timed to respiration and aredetected and marked in FIG. 19B as a relatively long microarousal in theepochs staged as sleep.

FIGS. 20A-20D include data of an example physiological signal pattern ofan EMG power bursts. FIGS. 20A-20D may be illustrative of signalpatterns of repetitively timed EMG power burst patterns that may beindicative of a ventilator distress (e.g., extreme ventilatoryasynchrony) and/or central sleep apnea associated with arousals. Forexample, FIGS. 20A-20D depict EMG power burst patterns includingmultiple power bursts on time scales of 1 hour (FIG. 20A), 30 min (FIG.20B), 10 min (FIG. 20C), and 30 s (FIG. 20D). FIGS. 20A and 20B identifyconsistent patterns of EMG bursts being automatically identified asmicroarousal events (e.g., shown as grey boxes in the EEG band). FIG.20C illustrates that a shorter time scale (e.g., 10 minutes) maybeneficially permit detection of the consistency of the EMG burstpattern. For example, the example EMG events of FIG. 20C are depicted atleast 10 sec in duration and occur repetitively every 30 seconds. Thus,the systems herein may be configured to determine that the EMG burstsare not correlated with each breath (e.g., during loud snoring), becausebreathing occurs between 8 and 24 times per minute. The EMG power, whenviewed on an even shorter time scale (e.g., 30 sec of FIG. 20D),presents four unique EMG excursions within each EMG event (e.g., 4 timesin ˜10 sec). In some embodiments, SDB may be ruled out based on previousknowledge or data indicating a patient is mechanically ventilated. Therepetitive timing of the illustrated microarousals may be suggestive ofan association with the mechanical ventilation, possibly extremeasynchrony, central sleep apnea related arousals, or irritation from theventilation tube when swallowing. In this case, further investigationmay be warranted (e.g., via recommendations and/or interventions inaccordance with FIGS. 5 and/or 7) because the abnormal EMG patterns(e.g., as determined in FIG. 15) indicate the patient may be unable tofall asleep. Both of these examples of identified abnormal signalcharacteristics can be computationally detected and identified by acomputer system 390, computer system 560, and/or mobile device 550 basedon analysis physiological signal patterns. Similarly, these examples ofidentified abnormal signal characteristics can be visually detected by acaregiver based on the unexpected sleep stage, presentation of automatedfeature extractions, and/or how the signal information is presented by agraphical user interface (as described below in connection to FIGS.29-31). Access to the staging of awake when the patient should be asleepand access to the signal patterns in near real time, while observing thepatient, may facilitate determination of a cause of the EMG burstactivity and implementation of the appropriate intervention.

Returning to FIG. 8, in some embodiments, undetected abnormalneurological signal patterns, such as burst suppression (e.g., step 840)and non-convulsive epileptiform activity (e.g., step 850) maycontributes to a slower recovery or increased risk of mortality topatients. In conventional EEG systems, these abnormal patterns are notgenerally detected because EEG is not routinely monitored.Advantageously, the DAU 110 may provide inexpensive and ease of use fornon-experts to affix the sensors to the patient (as described above) toachieve the continuous monitoring of these abnormal neurologicalactivities, as well as for monitoring circadian rhythm and other sleepactivity described herein. One skilled in the art will recognize thatacquisition of EEG from a limited number of sensor sights (e.g.,frontopolar EEG) does not replace the topographic information obtainedfrom a dense sensor array (e.g., standard 10-20 montage). Although someabnormal neurological activity will not be detectible with a limitedsensor montage depicted in FIG. 1 and source localization is notpossible, the described system capable of or continuous monitoring forgross abnormal neurological activity, in addition to monitoringsleep/wake.

Burst suppression is an epileptiform signal pattern that can beassociated with poor recovery outcomes as well as with heavy doses ofsedatives. FIGS. 21A-23B illustrate data of example physiological signalpatterns of abnormal burst suppressions. In some embodiments, FIGS.21A-23B may be indicative of the burst-suppressions identified, forexample, at step 840 of FIG. 8. For example, these examples may beindicative of abnormal burst suppression obtained from the frontopolarsites and readily detectible in one or more of the various power bandpatterns (e.g., as identified in FIG. 15). As described above, accordingto some embodiments, the acquired physiological signal data can beobtained by the DAU 110 and downloaded to an external computer system390 for processing and identification or, in some embodiments, byfirmware included on DAU 110. Alternatively, or in combination, the datamay be displayed by the external computer system 390 for visualinspection and identification by users.

FIGS. 21A-21D illustrate example signal patterns of burst suppressionincreasing smaller time scales, for example, of 1 hour (FIG. 21A), 30min (FIG. 21B), 10 min (FIG. 21C), and 30 s (FIG. 21D). Such burstsuppression may be present in alpha and sigma power bands characterizedby automated scoring as cortical arousals and sleep spindles. Duringburst suppression and/or isoelectric activity, the power across allbands may approach zero with subsequent bursts of alpha and sigma powermeasurements. These abnormal EEG burst patterns can be can bemisclassified as either sleep spindles or cortical arousals (e.g., asshown in FIG. 21D) and result in an epoch being auto-staged non-REM,because the prominent characteristic of such burst suppression are alphaand sigma power excursions. The combination of low power interspersedwith detected arousals and spindles may be utilized to identify burstsuppressions (e.g., step 840). In some embodiments, alpha burst and/orsigma bursts (or both) can be identified by the systems described hereinand used to detect arousals and sleep spindles. Thus, such bursts inalpha and/or sigma power may be combined with one or more additionalidentifications (e.g., detect rapid increases and/or decreases of powerbands as well as periods of suppressed) to facilitate burst suppressiondetection. In some embodiments, for example, the systems and devices (orcaregivers in some embodiment's) may be configured to recognizerepetitive cortical arousals and spindles coupled with power spectrapatterns showing high voltage bursts that alternate with coma-like brainactivity may facilitate detection of burst suppression activity. In someembodiments, the presence of such identifying characteristics over atime equal to or greater than a pre-determined threshold time (e.g., 30seconds, 40 seconds, or other as desired time scale for the specificapplication) may be further indicative of detected burst suppressionactivity.

In another embodiment, alone or in combination, abnormal EEG activity(e.g., as identified in FIG. 15) can manifest as burst patterns of betapower. Recognition of beta bursts during visual inspection of the EEGsignal may be more difficult than evaluating the relative powercharacteristics on different time scales. In some embodiments, burstoscillations may be most noticeable in beta power that coincides withlow amplitude bursts in sound. For example, FIGS. 22A and 22B illustratedata of example physiological signal patterns that may be used toidentify burst patterns of beta power. FIG. 22A illustrate that themagnitude of the beta bursts, relative to the sigma power (whichinfluenced the staging as Light N2), may be recognizable when viewed ona 10 min time scale opposed to a shorter time scale (e.g., 30 seconds ofFIG. 22B). When viewed on the shorter scale, beta bursts synchronizedwith the breathing can be detected. The temporal synchrony of beta powerbursts and snoring (in the sound signal) can indicate aneurophysiological response to pain.

In some embodiments, the systems and methods herein may be configured todetect when epileptiform burst activity occurs in only one side of thebrain (e.g., one of the three EEG waveforms is substantially differentfrom the other two). In some embodiments, patterns of large unilateralamplitude bursts of sigma and alpha power may be characterized as longcortical arousals. For example, FIGS. 23A and 23B include data ofexample physiological signal patterns that may be indicative ofunilateral burst oscillations. In some implementations, unusualunilateral burst oscillations can be detected by comparing one or moreof the power bands with one or more of the other power bands, where suchcomparison indicates burst activity in only some of the bands. Forexample, unusual unilateral burst oscillations can be apparent in thebeta, alpha and sigma power bands (e.g., as shown in 10 min time scaleof FIG. 23A). These aberrant patterns may be visible in the differentialEEG channel and in one or more of the other referential channels. Forexample, FIG. 23B shows that the REOG referential band is substantiallysimilar to the aberrant patter of the differential EEG channel, whilethe LEOG signal does not. However, the reverse may also be detected orother bands may lack the aberrant pattern. In some cases, this patternmay be detectible by the abrupt increase in alpha and sigma power whenviewed on the longer time scale (e.g., 10 minutes of FIG. 23A) and onshorter time scale (e.g., 30 seconds of FIG. 23B). Depending on thefrequency characteristics of the epileptiform activity, the aberrantpattern can be auto-detected as a cortical arousal (e.g., >3 secs ofelevated alpha activity) as show in FIG. 23B. In various embodiments,automated detection algorithms to be applied to the patient in responseto the periodicity of these power bursts, thereby identifying the burstsand initiating a corresponding intervention (e.g., FIG. 7).Alternatively, the signal patterns may be detectible by visualinspection based on the capability to review the power signals ondifferent time scales and/or the information provided with thecharacterization of the signals for sleep staging (e.g., FIG. 6).

Normal, artifact free EEG activity typically includes delta power thatbegins at 2 and beta power that ends at 40 Hz with a maximum amplitudeof approximately less than or equal to 75 μV. As much as 70% ofnon-convulsive seizure activity is detectable from frontal EEG leads.Epileptiform seizure activity is typically high frequency and largeamplitude (e.g., >100 μV), similar to EEG artifact that can berecognized by automated detection of large amplitude, short durationchanges in the signal waveform.

In some embodiments, large amplitude epileptiform activity may bedetected as artifacts and marked (e.g., FIGS. 24A-24C below), with theremaining spikes characterized as bursts in EMG (and other bands) andautoscored as microarousals. FIGS. 24A-24C include data of examplephysiological signal patterns that may be used to identifynon-convulsive seizure activity. In some embodiments, FIGS. 24A-24C maybe indicative of the non-convulsive seizure activity identified, forexample, at step 850 of FIG. 8. For example, FIGS. 24A-24C illustrateexamples of seizure spikes determined to be non-convulsive based in parton an absence of gross head movement. FIG. 24A provides an example ofwaveforms on a 30 min time scale identified as an artifact detection incombination with very large bursts of EMG power (e.g., high amplitudeartifact). Furthermore, as shown in FIG. 24A, there is little to no headmovement detected corresponding to the artifact. Thus, in oneembodiment, visual inspection of the waveforms on a short time scale(e.g., 30 second of FIG. 24C) and at +/−150 μV scale may confirm theidentified artifact is seizure activity. Similarly, automated inspectionexecuted by the external computer system 390 may identify the artifactand a corresponding time of the event, compare this with head movementdata associated with that same time period, and if no head movement isdetected, identify the artifact as seizure activity. Generally, theamplitude of the signal in the EEG channel is greater than the power ineither the LEOG or REOG channels (due to inter-electrode distance andcommon mode rejection). When the EEG signal is of lower magnitude, ascompared to the LEOG and REOG signals, it's likely a result of abnormalneurological activity or artifact.

In some embodiments, seizure activity may be further identified based inpart on a bilateral characteristic of the epileptiform activity. Forexample, FIG. 24B illustrates seizure activity apparent in thereferential signals (e.g., labeled LEOG and REOG in this example) butnot in the differential channel (e.g., labeled EEG) due to amplifiercommon mode rejection. Furthermore, despite the attenuated differentialsignal, the seizure activity causes a wide spectrum of burst activity(e.g., affects the alpha, sigma, beta and EMG power bands). Thus, thepresence of one or more of these identified features may be indicativethat the epileptiform activity is bilateral.

Healthy sleep is comprised of cycles typically ranging from 60 to 120minutes in length, and each sleep cycle is typically comprised of 30 secepochs transitioning from non-REM sleep (Stages N1, N2 and N3) and REMsleep. The systems and methods described herein may be used to monitorphysiological signals during acquisition, for example, by the DAU 110(e.g., a monitor mode) to evaluate either sleep or abnormalneurophysiology. In a second mode the systems and methods herein may beused offline for inspection and/or staging of sleep after the signalshave been acquired (e.g., a review mode). In some embodiments, thereview mode may be automated using the integrated system of FIG. 5 asdescribed herein. In some embodiments, these modes are independent,e.g., it may not possible to simultaneously monitor and review theacquired physiological signals. This may be, in part, because abnormalbrain activity is much more variable, thereby necessitating an extendedperiod of data collection. In some embodiments, abnormal signals may bepresent in extended periods of similar abnormal activity (e.g., ASWA) orbrief episodes of different types of abnormal activity (e.g., convulsiveor non-convulsive seizure activity). Recognizing brief episodes ofabnormal brain/sleep activity that would benefit from an interventionrequires the capability to simultaneously compare the wave forms as theyare being acquired (monitor) in the context of previously recordedsignals (review).

FIG. 25A-25E illustrates data of an example of physiological data withtransitions across different abnormal physiological signal patterns.FIG. 25 presents a sequence associated with convulsive seizure activitydetected by the signal patterns associated with head movement. FIG. 25Aillustrates a 30 min epoch with four segments 2510, 2520, 2530, and2540. FIGS. 25B-E illustrate the segments 2510-2540 extracted anddisplayed at a 30 s time scale. FIG. 25B illustrates an extended periodof ASWA which is staged abnormal N3. The period is followed by anepisode of convulsive seizure activity, observed by large amplitudeartifact spikes in the frontal EEG wave forms which are illustrated andcoupled with extreme gross movement (e.g., FIG. 25C). FIG. 25Dillustrates signals associated with the end of the ictal phase. Theelectrical seizure activity in the brain has ended, but visible symptoms(e.g., head movement) may persist. FIG. 25E illustrates a postictalperiod comprised of slow waves and low amplitude EEG indicative of lightN2.

FIGS. 25A-25D provide an example as to why it may be beneficial to viewthe current and previous power signals, sleep staging and wave form datasimultaneously while also transitioning among and between different timescales. Furthermore, a capability to be monitor and review on selectabletime scales would be beneficial. Once a potentially abnormal period isdetected on a long duration time scale, the user may then zoom in toconfirm the pattern type. Accordingly, a graphical user interface (GUI)is provided herein, as described in greater detail below in connectionto FIGS. 29-31, that provides user interactions for quickly andefficiently searching through currently and/or previously acquired datato determine the frequency and duration of these types of abnormalconditions. In various embodiments, it may be beneficial to use longerduration time scales when searching for abnormal patterns across, forexample, an 8 or 12 hour hospital shift or across a 24 hour circadiancycle.

In some embodiments, the physiological signals acquired by the DAU 110may be used to identify sleeping disorders affecting a patient's abilitysleep normally. For example, the signals characterized and identified inFIG. 13 may be indicative of such sleeping disorders. Hospitalizedpatients may be at greater risk for developing insomnia and/or acircadian rhythm disorder, which in turn can contribute to the onset ofdelirium. Both of these sleep disorders can be impacted by interruptedsleep during the night and/or intermittently during the day and night,rather than having sleep consolidated during nocturnal hours.Furthermore, such disorders may interrupt the normal progression throughthe various sleep stages (e.g., awake to NREM into REM and back). Thus,the DAU 110 in combination with the methods described in, for exampleFIGS. 8-27 may facilitate continuous and real-time monitoring of thepatient's sleep patterns. For example, the DAU 110 may collect data fromthe EEG sensors 310 and process the signals, as described throughoutthis disclosure, to assist with monitoring the patients sleep cycle. TheDAU 110 may detect transitions between the sleep stages that areirregular when compared with a normal sleep cycle. Thus, caregivers (orother automated systems as described herein) may utilize themeasurements to identify when a patient is not sleeping enough orexperiencing an interruption of their normal sleep cycle. In someembodiments, an appropriate physician may be consulted to administersedatives or other means to induce and/or steer the patient into adesired sleep stage. Alternatively, the systems described herein may beconfigured to administer (e.g., automated release of a sedative inaccordance with recommendations of FIG. 7) or otherwise steer thepatient into a desire sleep stage. Example devices and methods ofsteering a patients sleep stage is described in greater detail, forexample, in U.S. Pat. Nos. 8,628,462; 8,784,293; and 8,932,199, all ofwhich are hereby incorporated by reference in their entirety.

FIGS. 26 and 27 provide flow charts of example processes 2600 and 2700,respectively, for monitoring a patient's circadian rhythm to improve thequality of sleep. For example, the process 2600 and/or 2700, eitheralone or in combination, may be implemented using, for example, the DAU110 and/or the integrated system of FIG. 5 in a hospital, ICU, or otherenvironment that may impact a patient's ability to sleep to reduce therisk of delirium and speeds recovery by hospitalized patients bymonitoring sleep/wake patterns so that natural circadian rhythms can bemaintained. In some embodiments, either of process 2600 and/or 2700 maybe implemented as part of step 825 of FIG. 8 or as a separate process.

In several embodiments, the systems described herein may be capable ofmonitoring a current sleep state, previous sleep states, and arousalsfrom sleep, and implement the any one or more of the features describedthroughout this disclosure to, for example, discourage sleep during thedaytime, restrict daytime naps to the hours of 2 and 4 PM, and/orencourage consolidated sleep during the nighttime, or a combinationthereof.

For example, process 2600 may be implemented to discourage and/orrestrict sleep during the day. Process 2600 beings by acquiring (step2610) and characterizing (step 2620) physiological signals of a patient.In some embodiments, steps 2610 and step 2620 may be similar to steps805 and 810 of FIG. 8, respectively. The characterized signals of step2620 are then monitored for abnormal neurological patterns (step 2630)and treat accordingly. For example, abnormal neurological patterns maybe any one or more of the abnormal features identified in either FIG. 13and/or FIG. 15. At step 2640, the impact of the medication on sleepstages may be monitored. For example, the DAU 110 may monitor sleepstages as described above and correlated with the medication (e.g.,sedative and/or analgesia) type, dosage, and/or timing of administratingsuch to derive an impact thereof. In some embodiments, to reduce thelikelihood of daytime napping in a hospitalized environment the, dosageof sedative can be decreased. Alternatively or additionally, at step2650, the environmental stimuli that inhibit or otherwise affect sleepmay be monitored. For example, environmental conditions can be modifiedto discourage sleep during daytime hours, for example, by allowingenvironmental noise, timing of visitations and/or human interactions. Insome embodiments, light exposure may be modified (e.g., step 2660) toaffect the patient's sleep. For example, exposure to certain wavelengthsof light (e.g., blue light) may be used to inhibit melatonin productionin the patient. Furthermore, in some embodiments, the frequency and/orduration of napping may be monitored and modified to control daytimenapping (step 2670).

During nocturnal hours, process 2700 may be utilized to improve thequality and quantity of sleep. For example, when it is nocturnal hours,process 2700 begins by acquired (step 2710) and characterized (step2720) physiological signals of a patient. In some embodiments, steps2710 and step 2720 may be similar to steps 805 and 810 of FIG. 8,respectively. The characterized signals of step 2720 are then monitoredfor abnormal neurological patterns (step 2730) and treat accordingly.For example, abnormal neurological patterns may be any one or more ofthe abnormal features identified in either FIG. 13 and/or FIG. 15. Atstep 2740, the impact of the medication on sleep stages may bemonitored. For example, the DAU 110 may monitor sleep stages, asdescribed above, and correlated with the medication (e.g., sedativeand/or analgesia) type, dosage, and/or timing of administrating such toderive an impact thereof. In some embodiments, to increase thelikelihood of sleeping in a hospitalized environment the, dosage ofsedative can be increased. At step 2750, periodic arousals of thepatient may be monitored. For example, periodic patterns identified inFIG. 13 may be indicative of arousals. In some embodiments, combiningmodification of sleeping position with and/or in response to certainarousals may be used to intervene and otherwise reduce the severityperiod waking (e.g., reducing the severity of SDB in patients sleepingin supine position. At step 2760, temperature changes may be applied tothe patient (e.g., directly and/or indirectly) to control core bodytemperature and improve comfortability. For example, the application ofheat or cooling temperatures to the frontal regions of the patients facemay affect the patient's core temperature to induce sleep. At step 2770,the environmental stimuli that induce or otherwise affect sleep may bemonitored. For example, environmental conditions can be modified toencourage sleep during nocturnal hours, for example, by restrictingenvironmental noise, human interactions, and exposure to light toprovide environmental conditions more conducive to sleep.

While processes 2600 and 2700 are described as a series of steps

In various embodiments of the systems and methods described herewith,the detected physiological signals may be used to assess an amount ofREM sleep or detect early onset of REM. These assessments may be used tohelp with diagnosis of various ailments that may affect sleep. Forexample, assessment of the amount of REM sleep of a patient may be usedto diagnose depression while detecting early onset of REM may be usefulto diagnose narcolepsy. In some embodiments, physiological signalsdetected, for example, by the DAU 110 can also be used to minimize longterm traumatic stress syndrome (PTSD) symptoms. For example, suppressionof REM sleep immediately following a traumatic event may limit thecapability of the brain to encode the traumatic event into memory andthus impacts (e.g., reduce and/or minimize) the severity of thesymptom(s). As described above, it may advantageous to steer suchpatient's sleep stages out of or away from REM and into NREM withoutwaking the patients. In some embodiments, steering a patient's sleep maybe implemented using the systems and method described herein inconjunction with sleep guidance systems described above in connection toFIG. 5 above.

As an example, a patient may be admitted to the hospital following atraumatic event (e.g., a car accident or the like), and identified bycaregivers as at risk for PTSD. The DAU 110 can be applied to thepatient during their first night following the event, in either anin-patient or out-patient setting, to steer or otherwise control thepatient's sleep, for example, by shifting the patient out of REM sleepand into NREM sleep without causing the patient to wake up. Bysuppressing and/or avoiding REM, the effects of PTSD may be minimized,reduced, avoided, and/or treated without affecting the patient's sleepand, which could otherwise cause sleep deprivation.

In some embodiments, a patient identified as at risk for PTSD may beadmitted for overnight monitoring by hospital staff, and the DAU 110 maybe used to monitor the sleep stages of the patient. Upon detecting REMsleep or detecting a transition into REM sleep as described herein, acaregiver or an automated system (e.g., the sleep guidance system 530)may intervene as described above to lead the patient away from REM andinto NREM. In some embodiments, one or more stimuli may be applied tothe patient by, for example, the sleep guidance system 530 to induce thedesired the transition. For example, the stimuli may include, but notlimited to light, sound, smell, vibration, heat or cold, moisture,electric shock, and/or other stimuli that can be sensed by a sleeper.For example, in some embodiments, delivery of vibrotactile and/or bluelight may suppress REM sleep.

FIG. 28 provides a flow chart of an example process 2800 for reducingthe likelihood of developing symptoms of PTSD. For example, the process2800 may be implemented using, for example, the DAU 110 and/or theintegrated system of FIG. 5 in a hospital, ICU, in-patient setting,out-patient setting, or other environment that may impact a patient'sability to sleep to reduce the risk of sleep deprivation and reduceonset of PTSD.

In some embodiments, the process 2800 may be implemented followingidentification of a patient at-risk of PTSD. For example, process 2800may be implemented using the integrated system of FIG. 5 once a patienthas been admitted for overnight monitoring based, in part, onexperiencing a traumatic event.

Process 2800 beings by acquiring (step 2810) and characterizing (step2820) physiological signals of a patient. In some embodiments, steps2810 and step 2820 may be similar to steps 805 and 810 of FIG. 8,respectively. The characterized signals of step 2820 are then monitoredto detect the onset and/or presence of REM sleep stag (step 2830). Forexample, the DAU 110 may acquire physiological signals of the patientand, either alone or in combination with other components of integratedsystem of FIG. 5, may characterize the signals to detect and monitor thesleep stages of the patient. The monitored sleep stages may be based inpart on standard sleep staging rules and/or modified sleep staging rules(e.g., as described above in connection to FIG. 12). The process 2800monitors the sleep stages to detect the onset of and/or presence of REMsleep.

If either of onset of REM and/or presence of REM sleep is detected instep 2830, the process 2800 generates one or more feedback signals toavoid REM and delivers these signals to patient (step 2840). Forexample, the integrated system of FIG. 5 may determine feedback signalsin the form of stimuli as described above, and may generate the stimuli(e.g., light, sound, heat, smell, etc.) to steer the patient away fromand avoid REM sleep. Generating the stimuli may include deliver of thestimuli via the sleep guidance system as described above in connectionto FIG. 5 above.

At step 2850, the process 2800 monitors the patient's response to thedelivered feedback. For example, at step 2850, the patient'sphysiological signals may be monitored to ensure that the patient'ssleep stage has been successfully steered away from and/or out of REMsleep. In some embodiments, the monitoring of step 2850 may be doneusing the DAU 110 and/or integrated system of FIG. 5. In someembodiments, monitoring the response to the delivered feedback may besimilar to monitoring the patient for onset and/or presence of REM sleep(e.g., step 2830). If the patient begins to enter REM and/or iscurrently in REM during the monitoring step 2850, the process 2800returns to step 2840 to steer the patient out of and/or away from REM.Thus, the patient is continuously monitored for sleep stages and steeredaccordingly.

At step 2860, the therapeutic benefit may be assessed. For example, theDAU 110 may communicate physiological signals to one or more computersystems that may be used to assess the therapeutic benefit of process2800. In some embodiments, a medical care giver may be capable ofreviewing physiological signal data via, for example, a graphic userinterface such as the interface described below in FIGS. 29-31. In someembodiments, step 2860 is performed once the patient is successfullysteered away from and/or out of REM sleep. In some embodiments, alone orin combination, step 2860 may be performed in conjunction with either ofsteps 2840 and/or 2850 as well as during the feedback loop followingstep 2850.

FIGS. 29-31 illustrate embodiments of graphical user interfaces (GUI)for simplifying the presentation of and improving readability ofphysiological signals for use of monitoring such signal for abnormalsignal patterns and/or conditions. The embodiments of the GUIs describedherein may be used by users (e.g., caregivers, medical personnel,experts, etc.) to view physiological signals of a patient for use inmonitoring, interacting with, diagnosing, and otherwise performing theprocesses described in, for example, FIGS. 8-28 and throughout thisapplication. The GUI may be generated by one or more software modules ofthe DAU 110, computer systems 390, 550, and/or mobile device 550 of theintegrated system of FIG. 5, and may be displayed on a physical display(e.g., a touch panel display) of the computer systems 390, 550 and/ormobile device 550. The graphical user interface may comprise one or moredisplayable screens, such as the screens illustrated in FIGS. 29-31, aswell as other screens described and/or implied herein. While many of thescreens of the GUI will be individually described, the described screenssimply represent non-limiting, exemplary embodiments of the GUI. The GUImay be implemented in a different manner, with fewer or more of thedescribed screens and/or a different arrangement, ordering, and/orcombination of the described screens.

The GUI may be different for different systems, depending on one or morecharacteristics of the particular system used to view the GUI (e.g.,device type, display size, availability of particular input devices,processor speed, network speed, etc.). For example, the GUI displayed ona mobile device 550 and/or tablet computer may be simpler and/or morecompact than the GUI displayed on a computer system 560, in order toaccommodate the generally smaller display sizes on mobile devices. Asanother example, the GUI displayed on a computer system having a touchpanel display, configured to accept touch operations from a user'sfinger and/or stylus (e.g., touches/presses, long touches/presses,swipes, flicks, pinch-in operations, pinch-out operations, etc.), may bedifferent than the GUI displayed on a system that does not have a touchpanel display. Alternatively, the GUI may be identical across allsystems and/or device displays.

While user operations on the GUI will primarily be described hereinusing touch operations, it should be understood that analogous non-touchoperations may be used in place of any of the described touchoperations. For example, a short touch or tap may be replaced by aclick-and-release (e.g., by a mouse or other pointing device), a longtouch may be replaced by a click-and-hold or a hover, a swipe may bereplaced by a click-and-drag, a flick may be replaced by aclick-and-drag-and-release, and so on and so forth.

In addition, any of the user operations described herein, including theselection of icons or buttons or menu options, navigation (e.g.,scrolling, zooming in and/or out, transitioning between abnormal signalidentifiers, etc.), and/or the like, may be performed via voice input.For example, the computer system may receive a speech input via amicrophone, convert the speech input to a text representation viaspeech-to-text processes, and provide the text representation to the GUIas an operation input. For example, the computer system may match thetext representation to a command and execute the matched command.

It should also be understood that many, if not all, of the screens,regions, and/or panes described herein may be scrollable (e.g., byswiping up or down). Thus, if the time scale of a given epoch and/orsleeping event is too long to be viewable in a single region and/orpane, only a portion of the epoch and/or sleeping event may be initiallydisplayed, and the user may scroll through the signal data to viewpreviously collected data and scroll back to return to currentlyacquired data.

FIG. 29 illustrates an embodiment of a display screen 2900 of EEGactivity displayed using the GUI described herein. The informationpresented in the GUI may be representative of the physiological signalsof a patient and may be useful in monitoring normal and abnormal sleepand EEG activity as described throughout this disclosure. The GUI maysegment the display screen into a plurality of regions. As illustratedin the example shown in FIG. 29, the display screen is segmented into anupper half region 2910 and a lower half region 2920. The upper halfregion 2910 may include power spectra characteristics displayed on afirst time scale selected to optimize the visual recognition of normaland abnormal signal patterns. Similarly, the lower half region 2920 mayinclude other channels displayed on a second time scale selected tooptimize the recognition of normal and abnormal signal patterns in thosechannels. In some embodiments, the first and second time scales maydifferent as described below.

Additionally, in some embodiments, the upper half region 2910 and/orlower half region 2920 may be further segmented into a plurality ofpanes 2912, 2914, 2916, 2917, and 2918 in the upper half region 2910 anda plurality of pans 2922, 2924, and 2926 in the lower half region 2920.For example, FIG. 29 illustrates the delta and theta power signals(e.g., high amplitude power signals) are presented in pane 2912 and thealpha, sigma, beta and gamma/EMG signals (e.g., lower amplitude powersignals) are presented in pane 2914. The delta, theta, alpha, sigma,beta and EMG power can be displayed in a plurality of time scales viaselectable icons 2911, 2913, and 2915 in conjunction with recorded sound(e.g., pane 2916), position (e.g., pane 2917), movement (e.g., pane2918) and the classified sleep stage in pane 2930. For example, thedisplay screen 2900 includes an icon 2913 for displaying the upper halfregion at a 10 min time scale, an icon 2913 for displaying on a 30 mintime scale, and an icon 2915 for displaying on a 2 hour time scale. Insome embodiments, the signal data displayed in each of the panes may bechanged and otherwise modified to fit the particular application and/orscreen sizes used to view the data.

The lower half region 2920 may include the LEOG, REOG and EEG channelsdisplayed in one or more panes. For example, as shown in FIG. 29, theLEOG is displayed in the pane 2922, REOG in pane 2924, and EEG in pane2926. Similar to upper half region 2910, the lower half region maydisplay each pan in the 30 sec epoch (e.g., via selectable icon 2921), 5min (e.g., via selectable icon 2923) or 10 min (e.g., via selectableicon 2925) time scales.

The upper and lower half regions 2910 and 2920 may be independent withrespect to function (e.g., time scale and visual inspection). In someembodiments (not shown), the plurality of panes within each region maybe independent with respect to the other panes (e.g., displayed atdifferent amplitude ranges and/or time scales). The GUI may beconfigured to include a plurality of identifiers of patterns generatedbased the physiological signal as described throughout this disclosureused to stage sleep (e.g., identifiers 2930), including sleep spindles(e.g., identifier 2940) and/or cortical arousals (e.g., identifier2950). Thus, the user of the system may be able to view the variousphysiological signals at different time scales so to more easilyidentify normal and abnormal signal patterns. Furthermore, the GUIprovides an ease of switching between selectable time scales for ease ofcomparison between the various signals and analysis of previousphysiological signals so to identify prior or worsening patterns.

While a specific example is illustrated in FIG. 29, it will beappreciated that other arrangement are possible. For example, the numberof regions may be increased as desired to ease the display andidentification of abnormal patterns. Furthermore, the number of paneswithin each region may be increased and/or decreased as desired. Forexample, each power signal may be displayed in its own pane for separateanalysis and comparison. In some embodiments, the number of regionsand/or panes may be based in part on the device on displaying the GUI.For example, a mobile device may have a smaller screen than a computermonitor, thus fewer panes may be displayed as compared with the largerscreen. Furthermore, the time scales of icons 2911-2925 are forillustrative purposes only, and any desired time scale may be utilized(e.g., 8 hour, 12 hour, 10 s, etc.) as desired for the specificapplication.

FIGS. 30A and 30B illustrate example display screens 3010 and 3020 forpresenting power spectral densities on a first time scale (e.g., 30minutes in this example). FIG. 30A illustrates all power values (e.g.,delta, theta, alpha, sigma, beta, and EMG) presented in one displayscreen 3010, while FIG. 30B illustrates a subset of power bands (alpha,sigma, beta, and EMG) having similarly scaled amplitude magnitudes in adifferent display screen 3020. As illustrated in FIG. 30B, displayingthe subset of power bands having similar amplitude magnitudes permitsimproved detection of sleep stage transitions. Whereas, when all powerbands are combined and presented in a single screen (e.g., FIG. 30A) themagnitude of power in the delta and theta frequency ranges is so muchgreater than the alpha, sigma, beta and EMG power bands that may bedifficult to detect differences in the relative power characteristics.When the relationship between the alpha, sigma, beta and EMG isundetectable, it reduces the benefit of displaying the physiologicalsignals to help distinguish between non-REM from REM sleep. When thedelta and theta are presented in one pane and the remaining powerfrequencies are presented in a second pane (e.g., FIG. 30B), it myeasier to recognize the relative changes in sigma and beta power duringtransitions between non-REM and REM sleep and EMG power during sleep andwake. In one embodiment, FIG. 30B may be similar to pane 2914 of FIG.29.

FIGS. 30A and 30B may be illustrative of a non-limiting advantage of theGUI as described herein. For example, it may be advantages that powerbands with low amplitude values be displayed in a separate displayscreen (FIG. 30A) or separate pane (FIG. 29) than relatively higheramplitude values. In one embodiment, the signals may be separated andscaled into separate panes (e.g., FIG. 29), with the values in each paneautomatically scaled to accommodate the values of the individual beingmonitored. In another embodiment, alone or in combination, a user may beable to selectably switch between the presentations of the two sets ofsignals. In various embodiments the power spectra signals are displayedin combinations with alternative means for scaling the signals to enablevisual detection of normal and abnormal patterns.

To further assist with monitoring, the magnitude of the sound andmovement, head position, patterns of sleep stages, and cortical andsympathetic arousals may be presented on a selectable time scale (e.g.,pane 2916 of FIG. 29). For example, three frontopolar EEG signals areillustratively displayed with the signals from AF7-Fpz and AF8-Fpz,which contain the left and right ocular activity are default scaled to+75 μV to accommodate the typical range of the ocular signal amplitude.The signal obtained from AF7-AF8, labeled EEG, is presented with adefault of +50 μV. The values obtained from routine acquisition of theskin-sensor impedances from each site are presented to permit the userto be sure the device is properly affixed and collecting high qualitysignals. The automated detection of sleep spindles 2940 may be presentedas one or more stripes above the detected region of the EEG signal.Cortical arousals or other EEG feature characteristics (e.g., ocularactivity, detected sharp edges in the waveform, etc.) can be identifiedin accordance with the embodiments described herein and marked in thedisplay.

Mobile devices, such as tablets and mobile telephones, may be limited inscreen size and many physiological signals may need to be scaledsufficiently to allow visual interpretation. To accommodate bothrequirements, multiple screen presentations can be used to present thestandard signal information as well as alternative signal information(e.g., airflow or EMG signals). For example, the airflow signal maypermit a user to identify when an extubated patient has undiagnosed OSA.An EMG signal obtained from sensors affixed near the submental musclemay assist in the differentiation of REM from non-REM, or REM withoutatonia. An EMG signal obtained with sensors affixed near the diaphragmmuscle would enable a critical care worker identify acute respiratorydistress syndrome or identify ventilator asynchrony.

Accordingly, in some embodiments, an ALT icon 2960 may be provided inthe GUI for selectable switching presentation configurations. A firstdisplay screen may be set as a default configuration and an alternativedisplay screen may be set as an ALT configuration. Each configurationmay be used to display one or more of the physiological signals asdescribed herein. In an example embodiment, the first display screen maybe display screen 2900 and the user may interact with the ALT icon 2960to switch to a second display screen. An example second display screenis illustrated in FIG. 31, which includes upper half region 2910 andalternative lower half region 3020. The alternative lower half region3020 includes pane 3022 illustrating an airflow signal, pane 3024illustrating an EMG signal, and pane 3026 illustrating an EMG signal.Alternative lower half region 3020 may be similar to lower half region2920, but displaying a different configuration of signals. The signalsdisplayed in each region 2910, 2920, and 3020 may be different thanthose shown in the illustrative examples.

Thus, the ALT icon 2960 may enable presentation of a configurablealternative segment of signals. In one configuration, the ALT icon 2960may not appear in the upper half region 2910 because there is may not bean alternative configuration defined. For the lower half region 2920,the ALT icon 2960 may be used to select presentation of the airflowsignal in pane 3022, acquired by the DAU 110 configured with a nasalpressure transducer 280 and nasal cannula 160, in patients who are notintubated and mechanically ventilated. The pane 3024 may be selected forpresentation of an EMG signal. In one embodiment, the EMG signal isacquired from the submentalis muscles for use in visually confirming thedifferentiation of REM from non-REM sleep. One skilled in the art willrecognize that different combinations of signals can be presented and/oradjusted to different time scales. The device settings to configure theDAU 110 can be made locally on a tablet sized computer used to presentthe signals, or when interfaced to a desktop computer or a web-basedportal.

In one embodiment, time scales can be applied individually to thepresentation of the physiological signal for the upper and lower halfregions. For the signals in the upper half region 2910, thecharacteristics which differentiate normal from abnormal patterns may beoptimally viewed in long time windows, while the signals and signalcharacteristics useful in confirming abnormal frontopolar EEG may bepresented on a shorter duration time scale (e.g., lower half regions2920 and/or 3020). In an alternative embodiment, alone or incombination, the ratios of the power values sensitive to thedifferentiation of normal and abnormal neurophysiological patterns arepresented. Alternative embodiments alone or in combination, include theuse of machine learning techniques to incorporate other physiologicalpatterns, e.g., sound, heart rate, movement and/or position, to assistin the automated and/or visual differentiation of normal and abnormalpatterns.

A number of other features can be added to the GUI to assist in thedetection, monitoring or inspection of abnormal event periods. BecauseASWA is associated with sepsis, delirium and mortality, the percentageof recording time detected with ASWA can be tallied, summarized, andpresented in the GUI. Other feature characteristics can also besummarized to assist in the detection of abnormal conditions, e.g.,percentage of rejected signal time by channel, total and percentage ofsleep time, sleep spindle and cortical arousal event duration and/orevents per hour, etc.

A number of touch screen features may be used to scale the regionsand/or panes. The GUI may be configured to permit a user to interactionwith any one or more displayed panes independent from or withoutimpacting other panes. Thus, signals displayed in any one or more panemay be individually reviewed. For example, a user may interact with apane, for example, displaying the sleep stage information by swiping thepane to the left to cause the illustrated information shift or otherwisetransition to a selected point in the record of sleep stages (e.g., to apast or previous sleep stage relative to the current time). Similarly, auser may interact with a given pane to zoom in or zoom out using, forexample, a pinching or reverse pinching motion on a given pane. Thus,any of these signals can be presented on a shorter or longer durationtime windows, or in different combinations of signal panes to enablevisual monitoring and detection of normal and abnormal patterns.

In another embodiment, alone or in combination, another icon may beprovided to enable the presentation on the screen to immediatelytransition back in time to a period with ASWA or other auto-detectedabnormal period for more careful inspection. The icon(s) may also beused to transition back to periods based on other signal patterns, e.g.,excessively loud sounds, artifact, etc. Each time the icon is interactedwith, the presentation may transition back further in time in the recordto the next or earlier detected period. This may improve usability byproviding an alternative to having the user scroll sequentially backthrough the record during visual inspection. In one embodiment,thresholds, as described above for detecting the various abnormal signalpatterns, applied to the signal patterns used to detect the transitionpoints may be automatically applied by the software. In an alternativeembodiment, the thresholds can be manually set or selected using thesame approach described above for setting the device settings for theDAU 110.

The capability of the DAU 110 to acquire and the GUI to presentdifferent combinations of physiological signals may advantageouslyprovide caregivers a way to detect a source of an underlying problem.For example, in one embodiment a pitot tube may be used to extract anairflow signal from a mechanical ventilator for input into the nasalpressure transducer and surface electrodes placed over the diaphragm canbe used to acquire an EMG signal (e.g., shown in FIG. 31). Asynchronybetween the airflow signal and voluntary contraction observed in the EMGmay indicate ventilator timing issues. Synchronous patterns, on theother hand, may indicate central sleep apnea. Steady sound can indicatemore subtle breathing issues (e.g., discomfort from the endotrachealtube). In an alternative embodiment, the DAU 110 can be configured toacquire respiratory effort signals whereby the differences in the signalpatterns between the torso and abdomen belts are used to identifycentral sleep apnea. In each of these examples, the signal patterns canalert the caregiver of a previously undetectable problem that cancompromise the comfort or recovery of the patient.

The above description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the invention. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the general principles described herein can beapplied to other embodiments without departing from the spirit or scopeof the invention. Thus, it is to be understood that the description anddrawings presented herein represent a presently preferred embodiment ofthe invention and are therefore representative of the subject matterwhich is broadly contemplated by the present invention. It is furtherunderstood that the scope of the present invention fully encompassesother embodiments that may become obvious to those skilled in the artand that the scope of the present invention is accordingly not limited.

Furthermore, while each of the methods and processes described hereinare illustrated as a specific sequence of steps, in alternativeembodiments, any of the processes may be implemented with more, fewer,or a different arrangement and/or ordering of steps. Variousmodifications to these processes and methods will be readily apparent tothose skilled in the art, and the general principles described hereincan be applied to other embodiments described herein without departingfrom the spirit or scope of the invention.

What is claimed is:
 1. A method for managing sleep quality of a patientin a hospital environment, the method comprising: collectingphysiological signal data of the patient using a data acquisition unitelectrically coupled to at least one sensor affixed to the patient thatgenerates the physiologic signal data; using one or more hardwareprocessors executing instructions stored in a storage device: filteringthe physiological signal data into a plurality of frequency bandscorresponding to a plurality of power spectra waveforms; andcharacterizing an etiology of sleep quality of the patient based on acomparison of at least a first power spectra waveform of the pluralityof power spectra waveforms against at least a second power spectrawaveform of the plurality of power spectra waveforms, wherein the sleepquality of the patient is managed based on the characterized etiology ofsleep.
 2. The method of claim 1, further comprising detecting one ormore abnormal conditions indicative of a disruption to sleep quality ofthe patient based on a comparison of two or more power spectra waveformsof the plurality of power spectra waveforms.
 3. The method of claim 2,wherein the one or more abnormal conditions comprises characteristicswithin one or more of the plurality of power spectra waveformsindicative of at least one of abnormal slow wave activity, disorderedbreathing, frontal intermittent rhythmic delta activity, abnormal burstsuppressions, and non-convulsive seizure activity.
 4. The method ofclaim 2, wherein the patient is associated with a therapeutic treatment,the method further comprises performing an action by a sleep guidancesystem based on the detected one or more abnormal conditions to modifythe therapeutic treatment.
 5. The method of claim 1, wherein theplurality of power spectra waveforms comprises at least one of rhythmicactivity and transients.
 6. The method of claim 1, wherein the pluralityof power spectra waveforms comprises at least one or more of an alphapower spectra waveform, a sigma power spectra waveform, a beta powerspectra waveform, a delta power spectra waveform, a theta power spectrawaveform, a electromyographic (EMG) power spectra waveform, aelectroencephalographic (EEG) power spectra waveform, at least oneelectroocular (EOG) power spectra waveform, acoustic signal data, andmovement signal data.
 7. The method of claim 1, further comprising:identifying a plurality of sleep stages of patient over a time periodbased the subset of power spectra waveforms; determining the sleep stageof the plurality of sleep stages does not match an expected sleep stageof the patient; and in response to said determination, comparing one ormore power spectra waveforms with another one or more power spectrawaveforms to identify an abnormal signal pattern indicative of adisruption to sleep quality of the patient.
 8. The method of claim 7,wherein the sleep stage of the plurality of sleep stages does not matchan expected sleep stage due, in part, to at least one of an acute statusof the patient, medication administered to the patient, and atherapeutic treatment for the patient.
 9. The method of claim 1, whereinthe collected physiological signal data is communicated by the dataacquisition unit to an external computer system comprising the one ormore hardware processors, the method further generating a graphical userinterface on a display communicatively coupled to the external computersystem, the graphical user interface comprising a plurality of panes forseparately interacting with the physiological signal data.
 10. Themethod of claim 9, wherein the external computer system is configured toreceive physiological signal data of a plurality of patients from aplurality of data acquisition units electrically coupled to a pluralityof sensors affixed to the plurality of patients.
 11. The method of claim9, wherein the external computer system is associated with a specialist.12. The method of claim 1, further comprising, using a sleep guidancesystem: determining a current sleep state of the patient based on thecollected physiological signal data; determining a desired sleep statefor the patient based on one or more sleep staging rules, the currentsleep state, and an acute status of the patient, wherein the one or moresleep staging rules are configured to determine a desired sleep statethat improves recovery time from the acute status; and executing anaction to guide the patient toward the desired sleep state if desiredsleep state is different from the current sleep state.
 13. The method ofclaim 1, wherein the hospital environment is one of at least anintensive care unit and an emergency room.
 14. The method of claim 1,further comprises: streaming the collected physiological signal data toa computer system; and rendering the collected physiological signal dataon a display using a graphical user interface, the physiological signaldata presented in a plurality of panes.
 15. The method of claim 14,wherein the plurality of panes comprises at least a first pane fordisplaying a first power spectra waveform on a first time scale and asecond power spectra waveform on a second time scale, wherein the firstand second time scales are selected to facilitate comparison of at leastthe first power spectra waveform with the second power spectra waveform.16. The method of claim 1, wherein the at least one sensor comprises aplurality of sensors less than a full 10-20 montage of sensors.
 17. Asystem for managing sleep quality of a patient, the system comprising: adata acquisition unit electrically coupled to at least one sensoraffixed to the patient, wherein the data acquisition unit collectsphysiological signal data of the patient generated by the at least onsensor; at least one hardware processor; and a storage device coupled tothe at least one hardware processor and the data acquisition unit, thestorage device storing instructions that, when executed by the at leastone hardware, are operable to: filter the physiological signal data intoa plurality of frequency bands corresponding to a plurality of powerspectra waveforms; and characterize an etiology of sleep quality of thepatient based on a comparison of at least a first power spectra waveformof the plurality of power spectra waveforms against at least a secondpower spectra waveform of the plurality of power spectra waveforms,wherein the sleep quality of the patient is managed based on thecharacterized etiology of sleep.
 18. The system of claim 17, wherein theinstructions are further operable to detect one or more abnormalconditions indicative of a disruption to sleep quality of the patientbased on a comparison of two or more power spectra waveforms of theplurality of power spectra waveforms.
 19. The system of claim 17,further comprising an external computer system communicatively coupledto the data acquisition unit and comprising a display, wherein thecollected physiological signal data is communicated to the externalcomputer system, and the instructions further operable to generate agraphical user interface on the display, the graphical user interfacecomprising a plurality of panes for separately interacting with thephysiological signal data.
 20. The system of claim 19, furthercomprising a cloud server communicatively coupled to the dataacquisition unit for receiving the physiological signal data, the cloudserver accessible by one or more remote computer systems for retrievingat least a portion of the physiological signal data.